what is the meaning of technology in thesis

Technology Thesis Statement

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The dynamic world of technology continually shapes our daily lives and future. Writing a compelling thesis statement about technology means delving deep into the nuances of innovation, foreseeing its implications, and presenting a clear, concise perspective. Crafting the perfect statement requires a keen understanding of your topic, its relevance, and the message you wish to convey. Below, we will explore examples of technology-related thesis statements, provide tips on how to hone them, and guide you in encapsulating the essence of your research.

What is the Technology Thesis Statement? – Definition

A technology thesis statement is a concise summary or main point of a research paper, essay, or dissertation related to a technology-focused topic. It establishes the central theme, position, or argument that the author intends to communicate, providing readers with a clear overview of what the subsequent content will address. This research paper thesis statement is essential in guiding the flow and coherence of the piece, ensuring that the content remains relevant to the proposed topic.

What is an example of a Technology thesis statement?

“With the rapid evolution of wearable technology, there is a compelling need to address the associated privacy concerns, arguing that without comprehensive regulations, users’ personal data could be at significant risk.”  You should also take a look at our  middle school thesis statement .

100 Technology Statement Examples

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Technology concise thesis statements encapsulate the essence of tech-focused research papers or essays, presenting a concise argument or perspective on a specific technological development, trend, or challenge. These statements guide the reader’s understanding, giving clarity and direction to the narrative.

• Artificial Intelligence : “The integration of AI in healthcare can revolutionize patient diagnosis, but ethical constraints need addressing.”
• Virtual Reality : “Virtual reality’s potential in education extends beyond immersion, offering tailored learning experiences.”
• Blockchain : “Blockchain technology, while disruptive, promises to make financial transactions more transparent and secure.”
• Cybersecurity : “The rise of IoT devices demands stronger cybersecurity measures to prevent unprecedented breaches.”
• Biotechnology : “CRISPR technology might hold the key to genetic disorders, yet its ethical implications are vast.”
• E-Commerce : “The shift to e-commerce has fundamentally changed consumer behavior, prioritizing convenience over brand loyalty.”
• 5G Technology : “The deployment of 5G will enhance IoT capabilities, but infrastructure challenges persist.”
• Green Technology : “Solar panel advancements are crucial for sustainable energy but require policy support for widespread adoption.”
• Robotics : “Robotic automation in manufacturing accelerates production but poses employment challenges.”
• Wearable Tech : “Wearables are transforming health monitoring, but data privacy remains a significant concern.”
• Quantum Computing : “While quantum computers promise to solve complex problems in seconds, they also pose threats to current encryption methods.”
• Space Exploration : “The commercialization of space travel opens new frontiers for tourism but also raises environmental and safety concerns.”
• Augmented Reality : “Augmented reality in retail can enhance customer experience, yet it challenges traditional shopping norms.”
• Drones : “The proliferation of drone technology in delivery services improves efficiency but brings forth airspace regulation issues.”
• Nano-Technology : “Nanotechnology in medicine offers targeted drug delivery but has unexplored long-term effects on human health.”
• Self-Driving Cars : “Autonomous vehicles could drastically reduce traffic accidents, but their integration requires comprehensive legal frameworks.”
• Smart Cities : “Smart cities optimize urban living conditions; however, they highlight disparities in digital access.”
• Edge Computing : “Edge computing decentralizes data processing, enhancing IoT performance, but it raises concerns about localized data breaches.”
• 3D Printing : “3D printing revolutionizes manufacturing and healthcare but challenges intellectual property rights.”
• Digital Assistants : “Voice-activated digital assistants streamline daily tasks but provoke debates on user surveillance and privacy.”
• Telemedicine : “Telemedicine democratizes healthcare access, yet questions arise about its efficacy compared to in-person consultations.”
• Big Data : “Big data analytics can transform industries, but the potential misuse of information is a growing concern.”
• Cloud Computing : “Cloud adoption offers businesses scalability and flexibility, though it introduces unique cybersecurity challenges.”
• Digital Currency : “Cryptocurrencies like Bitcoin could redefine financial systems, but their volatility and regulatory gray areas persist.”
• Gaming Technology : “Esports and gaming technology foster global communities, but they also spotlight issues of digital addiction.”
• Neural Networks : “Neural networks enhance machine learning capabilities but make algorithm decision-making processes more opaque.”
• Mixed Reality : “Mixed reality blends the best of AR and VR, offering innovative solutions in training but requires significant hardware investments.”
• Social Media Algorithms : “Algorithms on social platforms shape user behavior, leading to questions about influence and manipulation.”
• Broadband Technology : “Universal broadband access can bridge educational gaps, but infrastructural and cost barriers remain.”
• Digital Learning Platforms : “Online education platforms democratize learning but challenge traditional educational paradigms.”
• Agricultural Tech : “Smart farming through tech can optimize yields, but its cost can exclude small-scale farmers.”
• Mobile Banking : “Mobile banking boosts financial inclusion in developing nations but raises issues of digital literacy.”
• Chatbots : “Chatbots in customer service optimize responsiveness but can depersonalize the user experience.”
• Facial Recognition : “Facial recognition tech can enhance security measures but has sparked debates on privacy and misuse.”
• Deepfakes : “Deepfake technology, while impressive, poses significant threats to misinformation and trust in media.”
• Health Tech : “Wearable health devices offer real-time monitoring, yet there’s growing concern over data security and interpretation accuracy.”
• Marine Technology : “Underwater drones present opportunities for oceanic exploration, but their use raises environmental concerns.”
• Sustainable Tech : “Technological solutions to waste management are crucial for urban sustainability, but require societal behavior changes for maximum effectiveness.”
• Language Translation : “Real-time translation tools are bridging communication gaps, but can’t replace the nuance of human translators.”
• Online Privacy : “VPN services enhance online privacy, yet they introduce challenges in legal jurisdictions and data accountability.”
• Internet of Things (IoT) : “While IoT connects everyday devices, it also increases potential points of cyber vulnerabilities.”
• Haptic Technology : “Haptic tech holds potential in virtual training environments but demands rigorous testing for consistent real-world replication.”
• Renewable Energy Tech : “Wind energy is a clean alternative, yet its land use and noise pollution issues remain unresolved.”
• Genomic Editing : “While genomic editing can prevent hereditary diseases, its potential misuse in ‘designer babies’ raises ethical debates.”
• E-Learning : “Digital classrooms can provide education continuity during crises, but highlight inequalities in tech accessibility.”
• Wireless Charging : “The evolution of wireless charging technology promotes convenience but necessitates universal standardization.”
• Retail Tech : “Smart mirrors in retail enhance consumer experience but can potentially infringe on privacy rights if misused.”
• Data Storage : “Quantum data storage could revolutionize information keeping, yet the transition from classical methods is fraught with challenges.”
• Livestreaming Tech : “The growth of livestreaming platforms boosts creator economies, but presents issues of content moderation.”
• Digital Twins : “Digital twins in manufacturing optimize production processes, but require significant data management and interpretation efforts.”
• Animal Tech : “RFID tags in wildlife conservation assist in species monitoring but raise concerns about animal welfare and interference.”
• Thermal Imaging : “Thermal imaging in public spaces can enhance security, but its widespread use prompts privacy debates.”
• Financial Tech (FinTech) : “Digital-only banks provide unparalleled convenience, yet face skepticism over their ability to handle financial crises.”
• Audio Tech : “Spatial a in headphones creates immersive experiences, but its effects on auditory health are under-researched.”
• Nano-Biotechnology : “Nano-biotech in targeted drug delivery holds promise, but its long-term interactions with biological systems remain unknown.”
• Location-Based Services : “Geolocation tools in apps enhance user experience, but inadvertently contribute to data surveillance concerns.”
• Human-Machine Interface : “Brain-computer interfaces might redefine communication for the differently-abled, but they also present neuroethical dilemmas.”
• Gig Economy Platforms : “Tech-driven gig economies offer flexible employment, but often at the cost of job security and benefits.”
• Environmental Monitoring : “Satellite technology for environmental monitoring is crucial for climate change mitigation, but depends on international collaboration and data-sharing.”
• Entertainment Tech : “Augmented reality in entertainment redefines audience engagement, but challenges traditional content creation paradigms.”
• Food Technology : “Lab-grown meats could significantly reduce the environmental impact of livestock, but their societal acceptance and taste equivalency remain under scrutiny.”
• Telecommunication : “The transition to satellite-based internet services can enhance global connectivity but introduces space debris management challenges.”
• Digital Art and Media : “Digital art platforms democratize artistic expression, though they raise concerns over copyright and originality.”
• Fitness Tech : “Smart gyms utilize AI to personalize workout regimens, but their reliance on user data raises privacy issues.”
• Medical Imaging : “AI-driven medical imaging can enhance diagnostic precision, yet its integration demands rigorous validation against traditional methods.”
• Urban Mobility : “Electric scooters in urban centers promote green mobility, but their indiscriminate use poses pedestrian safety risks.”
• Adaptive Tech : “Adaptive technologies for the differently-abled democratize access, but their high costs can limit widespread adoption.”
• Cryptographic Tech : “Post-quantum cryptography aims to secure data against future quantum attacks, but its practical implementation remains challenging.”
• Travel and Navigation : “AR-based navigation tools can revolutionize travel experiences, but they demand robust infrastructure to prevent inaccuracies.”
• Event Technology : “Virtual event platforms offer global outreach, but they challenge the conventional understanding of networking and engagement.”
• Consumer Electronics : “Flexible electronics pave the way for innovative gadgets, yet their durability and recyclability are concerns.”
• Space Mining : “Space mining could answer Earth’s resource scarcity, but its feasibility and impact on space ecosystems are contentious.”
• Fashion Tech : “Smart fabrics offer dynamic design possibilities, but their production processes raise environmental questions.”
• Elderly Tech : “Tech solutions for the elderly improve quality of life, but require intuitive designs to ensure ease of use.”
• Cyber Physical Systems : “Integrating physical processes with computer-based algorithms promises efficiency, but challenges real-time adaptability.”
• Rehabilitation Tech : “VR in physical rehabilitation offers immersive therapy, but its long-term efficacy compared to traditional methods is under exploration.”
• Collaborative Platforms : “Cloud-based collaborative tools redefine workplace productivity, but their over-reliance can risk centralizing data control.”
• Quantum Sensing : “Quantum sensors could redefine detection limits in various fields, but their scalability in real-world applications remains a hurdle.”
• Learning Management Systems (LMS) : “LMS platforms facilitate organized e-learning, but their design must prioritize user-friendliness for diverse user groups.”
• Aerospace Tech : “Electric aircraft represent the future of eco-friendly travel, but the transition requires breakthroughs in battery technology.”
• Hydroponic Farming : “Tech-driven hydroponic systems can increase agricultural yield in urban areas, but the initial setup costs and energy consumption are deterrents.”
• Waste Management Tech : “Automated waste sorting can significantly enhance recycling rates, but its success demands public awareness and participation.”
• Digital Publishing : “E-books and digital publications increase accessibility, but they also challenge traditional publishing economics.”
• Therapeutic Tech : “Biofeedback apps promise personalized stress management, but their recommendations need backing by robust clinical research.”
• Molecular Electronics : “Molecular-scale electronics could miniaturize devices further, but their stability and manufacturing pose significant challenges.”
• Industrial IoT : “Integrating IoT in industries optimizes production and maintenance, but its seamless functioning demands strong cybersecurity protocols.”
• Photonics : “Photonics in data transmission offers higher speeds, but its integration into current infrastructure is complex.”
• Marine Energy : “Harnessing oceanic energy can be a renewable power solution, but its impact on marine ecosystems needs careful evaluation.”
• Prosthetics Tech : “Advanced prosthetics with AI integration promise life-changing mobility, but the cost of development and acquisition challenges their accessibility.”
• Resilient Infrastructure : “Smart materials in construction adapt to environmental changes, but the long-term sustainability and economic feasibility remain subjects of research.”
• Optogenetics : “Optogenetics holds transformative potential for neurological disorders, but its ethical application in humans is still debated.”
• Entertainment Streaming : “Streaming platforms are reshaping entertainment consumption, but they also spotlight issues of digital rights and royalties.”
• Water Purification Tech : “Nanotechnology in water purification can address global water crises, but its ecological impact requires close monitoring.”
• Transportation Tech : “Hyperloop transportation promises rapid transits, but the infrastructural and safety challenges are monumental.”
• Pedagogical Tools : “AI-driven pedagogical tools individualize learning, but there’s a risk of over-reliance and diminished human interaction.”
• Remote Work Tech : “Advanced collaborative tools enable effective remote work, but they also blur the lines between professional and personal boundaries.”
• Sensor Technology : “Smart sensors in agriculture optimize irrigation and reduce water wastage, but their implementation costs can be prohibitive for small-scale farmers.”
• Food Preservation : “Innovative food preservation technologies can reduce global food wastage, but their energy consumption and efficiency need optimization.”
• Gaming Interfaces : “Brain-computer interfaces in gaming promise immersive experiences, but their long-term effects on neurological health are underexplored.”
• Material Science : “Meta-materials can revolutionize optics and telecommunications, but their large-scale production and integration pose significant challenges.”

Technology Thesis Statement Examples for Argumentative Essay

As the digital age progresses, there’s a growing consensus about the pros and cons of technology’s integration into our daily lives. Argumentative essays thesis statement on technology often delve into the ethical and societal implications, pushing the boundaries of the debates even further.

• Social Media’s Impact : “While some argue that social media strengthens interpersonal relationships, it can also be held responsible for eroding face-to-face interactions and deepening feelings of social isolation.”
• Digital Dependency : “The increasing reliance on smartphones has jeopardized our cognitive abilities, leading to diminished memory recall and reduced attention spans.”
• Online Privacy : “In the digital age, online privacy has become an illusion, with corporations and governments frequently infringing upon personal data rights.”
• Virtual Reality : “Despite the immersive experiences offered by virtual reality, its overuse can blur the distinction between the real and virtual worlds, leading to psychological implications.”
• Technological Progress vs. Job Security : “Technological advancements, while driving efficiency and progress, also threaten traditional jobs, potentially leading to economic disparities.”
• Digital Currency : “Cryptocurrencies, despite their volatile nature, represent a significant shift in the financial landscape and have the potential to decentralize traditional banking systems.”
• E-books vs. Traditional Books : “While e-books offer convenience and accessibility, they can never replace the tactile experience and emotional connection readers have with physical books.”
• The Internet and Democracy : “The internet, although hailed as a tool for democratizing information, also presents threats like misinformation campaigns that can undermine democratic processes.”
• Tech Giants and Monopoly : “The unchecked rise of tech giants poses a threat to competition, potentially stifling innovation and enabling monopolistic behaviors.”
• Green Technology : “Investing in green technologies is not merely an environmental imperative but also an economic opportunity that promises both sustainable growth and job creation.”

Thesis Statement Examples for Technology in Education

Education has undergone tremendous transformation thanks to technology. The intersection of technology and education raises questions about equity, effectiveness, and the shaping of future minds.

• Digital Literacy : “Incorporating digital literacy in education is crucial, not just for technological proficiency but for navigating the modern world responsibly and critically.”
• Online Learning : “Online education, while offering flexibility and accessibility, can lack the personal touch and hands-on experiences that traditional classrooms provide.”
• EdTech in Early Childhood : “Introducing technology in early childhood education can foster creativity and adaptability, but it must not overshadow foundational learning experiences.”
• Gamification of Learning : “Gamifying education can increase student engagement, but there’s a risk of prioritizing rewards over actual knowledge acquisition.”
• Tech in Special Education : “Technology has the potential to revolutionize special education, offering tailored learning experiences to cater to individual needs.”
• Digital Distractions : “The integration of technology in classrooms, while beneficial, also brings the challenge of combating digital distractions and ensuring focused learning.”
• Open Source Learning : “Open-source educational resources can democratize education, but there’s a need to ensure the quality and credibility of these materials.”
• AR and VR in Education : “Augmented and virtual reality tools in education can offer immersive learning experiences, but their efficacy compared to traditional methods remains to be thoroughly evaluated.”
• Adaptive Learning Systems : “Adaptive learning technologies promise personalized education, but reliance on them must be balanced with human mentorship.”
• Digital Divide : “The push for technology in education must also address the digital divide, ensuring that students from all socioeconomic backgrounds have equal access.”

Thesis Statement Examples on Technology in Artificial Intelligence

The realm of artificial intelligence is a marvel of modern science and engineering, but it brings forth numerous concerns and speculations. Essays on AI and technology focus on the potential of machines surpassing human intelligence and the societal repercussions of such a possibility.

• Ethical AI : “As AI systems grow in complexity, there’s an urgent necessity to establish ethical guidelines that prioritize human values and safety.”
• AI in Warfare : “The integration of AI in military operations, while enhancing precision, raises alarming concerns about the lack of human judgment in life-and-death decisions.”
• Bias in Machine Learning : “Unchecked, machine learning models can perpetuate and amplify societal biases, necessitating rigorous audit processes before deployment.”
• AI and Employment : “The rise of automation and AI in industries risks a significant displacement of the workforce, highlighting the need for societal adaptation and job retraining.”
• Emotion AI : “Artificial Intelligence designed to recognize and respond to human emotions could revolutionize industries, but also brings concerns about privacy and emotional manipulation.”
• Singularity : “The potential for an AI singularity, where AI surpasses human intelligence, necessitates preemptive safeguards to ensure the alignment of AI goals with humanity’s best interests.”
• AI in Healthcare : “While AI in healthcare can lead to more accurate diagnoses, it must complement, not replace, the critical thinking and empathy of medical professionals.”
• Deepfakes and Reality : “The advent of deepfake technology, driven by AI, challenges our trust in visual content, pressing for the development of verification tools.”
• AI and Creativity : “The surge of AI in creative fields, from art to music, questions the uniqueness of human creativity and the future role of AI as co-creators.”
• General AI vs. Narrow AI : “While narrow AI excels in specific tasks, the pursuit of general AI, mirroring human intelligence, presents unprecedented challenges and ethical dilemmas.”

Thesis Statement Examples on Medical Technology

The medical field has seen rapid technological advancements, leading to breakthroughs in treatment and patient care. Discussing medical technology often centers around its impact on the patient-doctor relationship and health outcomes.

• Telemedicine : “Telemedicine, while increasing healthcare accessibility, requires rigorous regulation to ensure the quality of care and the privacy of patient data.”
• Gene Editing : “CRISPR and other gene-editing technologies hold promise for eradicating genetic diseases, but they also raise ethical concerns about the potential misuse in creating ‘designer babies’.”
• Wearable Health Tech : “Wearable health devices empower individuals to monitor their health, but also bring concerns about data privacy and the accuracy of health information.”
• 3D Printed Organs : “3D printing of organs could revolutionize transplants, but the technology must first overcome challenges in biocompatibility and functionality.”
• Robot-Assisted Surgery : “Robot-assisted surgeries promise precision and minimized invasiveness, yet the high costs and training requirements present hurdles for widespread adoption.”
• Mental Health Apps : “Digital tools for mental health can democratize access to resources, but they cannot replace the nuanced care provided by human professionals.”
• Nanotechnology in Medicine : “The integration of nanotechnology in medicine offers targeted treatments and drug delivery, but long-term effects on the human body remain largely unknown.”
• Virtual Reality in Therapy : “VR therapies hold potential for treating phobias and PTSD, but research must ensure that virtual experiences translate to real-world recovery.”
• EHR (Electronic Health Records) : “While EHRs streamline medical data management, concerns arise about patient data security and system interoperabilities.”
• AI-driven Diagnosis : “AI-driven diagnostic tools can analyze vast data quickly, but they should act as aides to human clinicians, not replacements.”

Thesis Statement Examples for Technology Essay

General technology essays touch on the overarching theme of how technology shapes society, cultures, and personal interactions. These essays dive deep into both the boons and banes of technological innovation.

• Digital Age and Mental Health : “The digital age, while connecting the world, has also escalated mental health issues, prompting a deeper examination of our relationship with technology.”
• Augmented Humanity : “Biohacking and body augmentations, powered by tech, are pushing the boundaries of human capabilities but also raise ethical questions about self-modification and societal implications.”
• Cybersecurity : “In a hyper-connected world, cybersecurity is not just a technical challenge but a fundamental aspect of ensuring personal rights and national security.”
• Sustainable Technologies : “The rise of sustainable technologies is not a mere trend but a necessity to ensure the future survival and prosperity of our planet.”
• Digital Nomadism : “The evolution of remote work technologies has birthed the digital nomad culture, reshaping traditional perceptions of work-life balance and productivity.”
• Space Technologies : “Emerging space technologies, from satellite constellations to interplanetary exploration, hold the promise of reshaping our understanding of the universe and our place in it.”
• Tech and Pop Culture : “The infusion of technology into pop culture, from movies to music, reflects society’s struggles, aspirations, and dreams in the digital age.”
• Digital Archiving : “The practice of digital archiving is crucial not just for preserving history but for ensuring accountability in the digital era.”
• The Right to Disconnect : “As work and personal life boundaries blur due to technology, there’s a rising demand for the ‘right to disconnect’, ensuring mental well-being.”
• Tech in Urban Planning : “Smart cities, driven by technology, promise enhanced living experiences, but they also raise concerns about surveillance and the loss of privacy.”

Thesis Statement Examples for Technology in the Classroom

Classroom technology has redefined traditional teaching methodologies, leading to a new age of learning. Essays in this category often grapple with the balance between technology and traditional pedagogies.

• Digital Collaboration : “Collaborative tools in classrooms foster teamwork and communication but necessitate guidelines to ensure productive and respectful engagements.”
• Interactive Learning : “Interactive whiteboards and digital simulations can enhance understanding and retention, but educators must ensure they don’t become mere entertainment.”
• Classroom Analytics : “The use of analytics in classrooms promises personalized feedback and interventions, but raises concerns about student privacy and data misuse.”
• Digital Textbooks : “While digital textbooks offer dynamic content and portability, the potential loss of traditional reading skills and tactile learning must be addressed.”
• Flipped Classrooms : “Flipped classrooms, facilitated by technology, encourage student-centered learning at home, but require a redefinition of classroom roles and responsibilities.”
• Tech and Special Needs : “Assistive technologies in classrooms have democratized education for students with special needs, but teachers need training to utilize them effectively.”
• Student Engagement : “Gamified learning platforms can significantly increase student engagement, but there’s a risk of overemphasis on rewards over actual learning outcomes.”
• Distance Learning : “Technology has made distance learning feasible and expansive, yet the challenges of student isolation and self-regulation need addressing.”
• Digital Citizenship : “Teaching digital citizenship in classrooms is essential in the modern age to ensure students use technology responsibly and ethically.”
• Classroom VR : “Introducing virtual reality in classrooms can offer immersive educational experiences, but its efficacy and potential overstimulation issues need thorough research.”

What is a good thesis statement for technology?

A good thesis statement for technology succinctly captures your main argument or perspective on a specific technological issue. Such a statement should exhibit:

• Precision : Clearly articulate your viewpoint on the technological matter, ensuring it isn’t vague.
• Debate Potential : Present a point open to discussion or counterargument, not just a plain fact.
• Current Relevance : Address up-to-date technological advancements or concerns.
• Conciseness : Stay direct and avoid broad overviews.

Example: “Artificial intelligence in healthcare, while promising enhanced patient care, raises pressing ethical concerns.”

How do you write a Technology Thesis Statement? – Step by Step Guide

• Pinpoint a Specific Tech Area : Instead of a broad area like “technology,” zoom into niches: e.g., “Blockchain’s role in data security” or “Virtual Reality in education.”
• Undertake Preliminary Research : Grasp the current scenario of your selected area. Identify ongoing debates, breakthroughs, and challenges.
• State Your Assertion : Your research will guide you to a specific stance. This becomes your thesis’s foundation.
• Check for Debate Potential : Ensure that your assertion isn’t just stating the obvious but invites discussion.
• Maintain Brevity : Keep it succinct—usually, one to two sentences will suffice.
• Iterate : As your research or essay progresses, you might find the need to fine-tune your statement.

Tips for Writing a Thesis Statement on Technology Topics

• Stay Informed : With technology’s rapid pace, being up-to-date is essential. Your thesis should resonate with current technological dialogues.
• Steer Clear of Jargons : If your audience isn’t tech-centric, simplify or explain tech terms for clarity.
• Dive into Ethical Angles : Tech topics often interweave with ethical considerations. Tackling these adds depth.
• Solicit Feedback : Sharing your thesis with colleagues or mentors can offer new viewpoints or refinements.
• Employ Assertive Language : Words like “should,” “must,” or “will” give your statement authority.
• Remain Adaptable : If new evidence emerges as you write, be open to reworking your thesis slightly.
• Link to Broader Implications : Relating your tech topic to wider societal or global issues can offer added layers of significance.
• Ensure Clarity : Your thesis should have one clear interpretation to avoid reader confusion.

By honing these techniques and tips, you’ll be adept at formulating impactful thesis statements tailored to technology-centric topics. As technology continues to shape our world, the ability to critically and concisely discuss its implications is invaluable.  You may also be interested in our Analytical Essay thesis statement .

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Philosophy of Technology

If philosophy is the attempt “to understand how things in the broadest possible sense of the term hang together in the broadest possible sense of the term”, as Sellars (1962) put it, philosophy should not ignore technology. It is largely by technology that contemporary society hangs together. It is hugely important not only as an economic force but also as a cultural force. Indeed during the last two centuries, when it gradually emerged as a discipline, philosophy of technology has mostly been concerned with the meaning of technology for, and its impact on, society and culture, rather than with technology itself. Mitcham (1994) calls this type of philosophy of technology “humanities philosophy of technology” because it accepts “the primacy of the humanities over technologies” and is continuous with the overall perspective of the humanities (and some of the social sciences). Only recently a branch of the philosophy of technology has developed that is concerned with technology itself and that aims to understand both the practice of designing and creating artifacts (in a wide sense, including artificial processes and systems) and the nature of the things so created. This latter branch of the philosophy of technology seeks continuity with the philosophy of science and with several other fields in the analytic tradition in modern philosophy, such as the philosophy of action and decision-making, rather than with the humanities and social science.

The entry starts with a brief historical overview, then continues with a presentation of the themes on which modern analytic philosophy of technology focuses. This is followed by a discussion of the societal and ethical aspects of technology, in which some of the concerns of humanities philosophy of technology are addressed. This twofold presentation takes into consideration the development of technology as the outcome of a process originating within and guided by the practice of engineering, by standards on which only limited societal control is exercised, as well as the consequences for society of the implementation of the technology so created, which result from processes upon which only limited control can be exercised.

1.1 The Greeks

1.2 later developments; humanities philosophy of technology, 1.3 a basic ambiguity in the meaning of technology, 2.1 introduction: science and technology’s different relations to philosophy, 2.2 the relationship between technology and science, 2.3 the centrality of design to technology, 2.4 methodological issues: design as decision making, 2.5 metaphysical issues: the status and characteristics of artifacts, 2.6 other topics, 3.1 the development of the ethics of technology, 3.2.1 cultural and political approaches, 3.2.2 engineering ethics, 3.2.3 ethics of specific technologies, 3.3.1 neutrality versus moral agency, 3.3.2 responsibility, 3.3.3 design, 3.3.4 technological risks, encyclopedias, other internet resources, related entries, 1. historical developments.

Philosophical reflection on technology is about as old as philosophy itself. Our oldest testimony is from ancient Greece. There are four prominent themes. One early theme is the thesis that technology learns from or imitates nature (Plato, Laws X 889a ff.). According to Democritus, for example, house-building and weaving were first invented by imitating swallows and spiders building their nests and nets, respectively (Diels 1903 and Freeman 1948: 154). Perhaps the oldest extant source for the exemplary role of nature is Heraclitus (Diels 1903 and Freeman 1948: 112). Aristotle referred to this tradition by repeating Democritus’ examples, but he did not maintain that technology can only imitate nature: “generally technè in some cases completes what nature cannot bring to a finish, and in others imitates nature” ( Physics II.8, 199a15; see also Physics II.2, and see Schummer 2001 and this encyclopedia’s entry on episteme and techne for discussion).

A second theme is the thesis that there is a fundamental ontological distinction between natural things and artifacts. According to Aristotle ( Physics II.1), the former have their principles of generation and motion inside, whereas the latter, insofar as they are artifacts, are generated only by outward causes, namely human aims and forms in the human soul. Natural products (animals and their parts, plants, and the four elements) move, grow, change, and reproduce themselves by inner final causes; they are driven by purposes of nature. Artifacts, on the other hand, cannot reproduce themselves. Without human care and intervention, they vanish after some time by losing their artificial forms and decomposing into (natural) materials. For instance, if a wooden bed is buried, it decomposes to earth or changes back into its botanical nature by putting forth a shoot.

The thesis that there is a fundamental difference between man-made products and natural substances has had a long-lasting influence. In the Middle Ages, Avicenna criticized alchemy on the ground that it can never produce ‘genuine’ substances (Briffault 1930: 147). Even today, some still maintain that there is a difference between, for example, natural and synthetic vitamin C. The modern discussion of this theme is taken up in Section 2.5 .

Aristotle’s doctrine of the four causes—material, formal, efficient and final—can be regarded as a third early contribution to the philosophy of technology. Aristotle explained this doctrine by referring to technical artifacts such as houses and statues ( Physics II.3). The four causes are still very much present in modern discussions related to the metaphysics of artifacts. Discussions of the notion of function, for example, focus on its inherent teleological or ‘final’ character and the difficulties this presents to its use in biology. And the notorious case of the ship of Theseus—see this encyclopedia’s entries on material constitution , identity over time , relative identity , and sortals —was introduced in modern philosophy by Hobbes as showing a conflict between unity of matter and unity of form as principles of individuation. This conflict is seen by many as characteristic of artifacts. David Wiggins (1980: 89) takes it even to be the defining characteristic of artifacts.

A fourth point that deserves mentioning is the extensive employment of technological images by Plato and Aristotle. In his Timaeus , Plato described the world as the work of an Artisan, the Demiurge. His account of the details of creation is full of images drawn from carpentry, weaving, ceramics, metallurgy, and agricultural technology. Aristotle used comparisons drawn from the arts and crafts to illustrate how final causes are at work in natural processes. Despite their negative appreciation of the life led by artisans, who they considered too much occupied by the concerns of their profession and the need to earn a living to qualify as free individuals, both Plato and Aristotle found technological imagery indispensable for expressing their belief in the rational design of the universe (Lloyd 1973: 61).

Although there was much technological progress in the Roman empire and during the Middle Ages, philosophical reflection on technology did not grow at a corresponding rate. Comprehensive works such as Vitruvius’ De architectura (first century BC) and Agricola’s De re metallica (1556) paid much attention to practical aspects of technology but little to philosophy.

In the realm of scholastic philosophy, there was an emergent appreciation for the mechanical arts. They were generally considered to be born of—and limited to—the mimicry of nature. This view was challenged when alchemy was introduced in the Latin West around the mid-twelfth century. Some alchemical writers such as Roger Bacon were willing to argue that human art, even if learned by imitating natural processes, could successfully reproduce natural products or even surpass them (Newman 2004). The result was a philosophy of technology in which human art was raised to a level of appreciation not found in other writings until the Renaissance. However, the last three decades of the thirteenth century witnessed an increasingly hostile attitude by religious authorities toward alchemy that culminated eventually in the denunciation Contra alchymistas , written by the inquisitor Nicholas Eymeric in 1396 (Newman 2004).

The Renaissance led to a greater appreciation of human beings and their creative efforts, including technology. As a result, philosophical reflection on technology and its impact on society increased. Francis Bacon is generally regarded as the first modern author to put forward such reflection. His view, expressed in his fantasy New Atlantis (1627), was overwhelmingly positive. This positive attitude lasted well into the nineteenth century, incorporating the first half-century of the industrial revolution. Karl Marx, for example, did not condemn the steam engine or the spinning mill for the vices of the bourgeois mode of production; he believed that ongoing technological innovation allowed for the necessary steps toward the more blissful stages of socialism and communism of the future. A discussion of different views on the role of technology in Marx’s theory of historical development can be found in Bimber 1990. See Van der Pot 1985 [1994/2004] for an extensive historical overview of appreciations of the development of technology generally.

A turning point in the appreciation of technology as a socio-cultural phenomenon is marked by Samuel Butler’s Erewhon (1872), written under the influence of the Industrial Revolution, and Darwin’s On the Origin of Species (1859). Butler’s book gave an account of a fictional country where all machines are banned and the possession of a machine or the attempt to build one is a capital crime. The people of this country had become convinced by an argument that ongoing technical improvements are likely to lead to a ‘race’ of machines that will replace mankind as the dominant species on earth. This introduced a theme that has remained influential in the perception of technology ever since.

During the last quarter of the nineteenth century and most of the twentieth century a critical attitude predominated in philosophical reflection on technology. The representatives of this attitude were, overwhelmingly, schooled in the humanities or the social sciences and had virtually no first-hand knowledge of engineering practice. Whereas Bacon wrote extensively on the method of science and conducted physical experiments himself, Butler, being a clergyman, lacked such first-hand knowledge. Ernst Kapp, who was the first to use the term ‘philosophy of technology’ in his book Eine Philosophie der Technik (1877 [2018]), was a philologist and historian. Most of the authors who wrote critically about technology and its socio-cultural role during the twentieth century were philosophers of a general outlook, such as Martin Heidegger (1954 [1977]), Hans Jonas (1979 [1984]), Arnold Gehlen (1957 [1980]), Günther Anders (1956), and Andrew Feenberg (1999). Others had a background in one of the other humanities or in social science, such as literary criticism and social research in the case of Lewis Mumford (1934), law in the case of Jacques Ellul (1954 [1964]), political science in the case of Langdon Winner (1977, 1980, 1983) and literary studies in the case of Albert Borgmann (1984). The form of philosophy of technology constituted by the writings of these and others has been called by Carl Mitcham (1994) “humanities philosophy of technology”, because it takes its point of departure from the humanities and the social sciences rather than from the practices of science and engineering, and it approaches technology accepting “the primacy of the humanities over technologies” (1994: 39), since technology originates from the goals and values of humans.

Humanities philosophers of technology tend to take the phenomenon of technology itself largely for granted; they treat it as a ‘black box’, a given, a unitary, monolithic, inescapable phenomenon. Their interest is not so much to analyze and understand this phenomenon itself but to grasp its relations to morality (Jonas, Gehlen), politics (Winner), the structure of society (Mumford), human culture (Ellul), the human condition (Hannah Arendt), or metaphysics (Heidegger). In this, these philosophers are almost all openly critical of technology: all things considered, they tend to have a negative judgment of the way technology has affected human society and culture, or at least they single out for consideration the negative effects of technology on human society and culture. This does not necessarily mean that technology itself is pointed out as the principal cause of these negative developments. In the case of Heidegger, in particular, the paramount position of technology in modern society is rather a symptom of something more fundamental, namely a wrongheaded attitude towards Being which has been on the rise for almost 25 centuries. It is therefore questionable whether Heidegger should be considered as a philosopher of technology, although within the humanities view he is considered to be among the most important ones. Much the same could be said about Arendt, in particular her discussion of technology in The Human Condition (1958), although her position in the canon of humanities philosophy of technology is not as prominent as is Heidegger’s.

To be sure, the work of these founding figures of humanities philosophy of technology has been taken further by a second and third generation of scholars—in particular the work of Heidegger remains an important source of inspiration—but who in doing so have adopted a more neutral rather than overall negative view of technology and its meaning for human life and culture. Notable examples are Ihde (1979, 1993) and Verbeek (2000 [2005]).

In its development, humanities philosophy of technology continues to be influenced not so much by developments in philosophy (e.g., philosophy of science, philosophy of action, philosophy of mind) but by developments in the social sciences and humanities. Although, for example, Ihde and those who take their point of departure with him, position their work as phenomenologist or postphenomenologist, there does not seem to be much interest in either the past or the present of this diffuse notion in philosophy, and in particular not much interest in the far from easy question to what extent Heidegger can be considered a phenomenologist. Of particular significance has been the emergence of ‘Science and Technology Studies’ (STS) in the 1980s, which studies from a broad social-scientific perspective how social, political, and cultural values affect scientific research and technological innovation, and how these in turn affect society, politics, and culture. We discuss authors from humanities philosophy of technology in Section 3 on ‘Ethical and Social Aspects of Technology’, but do not present separately and in detail the wide variety of views existing in this field. For a detailed treatment Mitcham’s 1994 book still provides an excellent overview. A recent coverage of humanities philosophy of technology is available in Coeckelbergh’s (2020a) textbook. Olsen, Selinger and Riis (2008) and Vallor (2022) offer wide-ranging collections of contributions; Scharff and Dusek (2003 [2014]) and Kaplan (2004 [2009]) present comprehensive anthologies of texts from this tradition.

Mitcham contrasts ‘humanities philosophy of technology’ to ‘engineering philosophy of technology’, where the latter refers to philosophical views developed by engineers or technologists as “attempts … to elaborate a technological philosophy” (1994: 17). Mitcham discusses only a handful of people as engineering philosophers of technology: Ernst Kapp, Peter Engelmeier, Friedrich Dessauer, and much more briefly Jacques Lafitte, Gilbert Simondon, Hendrik van Riessen, Juan David García Bacca, R. Buckminster Fuller and Mario Bunge. The label ‘engineering philosophy of technology’ raises serious questions: many of the persons discussed hardly classify as engineers or technologists. It is also not very clear how the notion of ‘a technological philosophy’ should be understood. As philosophers, these authors seem all to be rather isolated figures, whose work shows little overlap and who seem to be sharing mainly the absence of a ‘working relation’ with established philosophical disciplines. It is not so clear what sorts of questions and concerns underlie the notion of ‘engineering philosophy of technology’. A larger role for systematic philosophy could bring it quite close to some examples of humanities philosophy of technology, for instance the work of Jacques Ellul, where the analyses would be rather similar and the remaining differences would be ones of attitude or appreciation.

In the next section we discuss in more detail a form of philosophy of technology that we consider to occupy, currently, the position of alternative to the humanities philosophy of technology. It emerged in the 1960s and gained momentum in the past twenty to twenty-five years. This form of the philosophy of technology, which may be called ‘analytic’, is not primarily concerned with the relations between technology and society but with technology itself. It expressly does not look upon technology as a ‘black box’ but as a phenomenon that should be studied in detail. It does not regard technology as such as a practice but as something grounded in a practice, basically the practice of engineering. It analyses this practice, its goals, its concepts and its methods, and it relates its findings to various themes from philosophy.

In seeing technology as grounded in a practice sustained by engineers, similar to the way philosophy of science focuses on the practice of science as sustained by scientists, analytic philosophy of technology could be thought to amount to the philosophy of engineering. Indeed many of the issues related to design, discussed below in Sections 2.3 and 2.4 , could be singled out as forming the subject matter of a philosophy of engineering. The metaphysical issues discussed in Section 2.5 could not, however, and analytic philosophy of technology is therefore significantly broader than philosophy of engineering. The very title of Philosophy of Technology and Engineering Sciences (Meijers 2009), an extensive up-to-date overview, which contains contributions to all of the topics treated in the next section, suggests that technology and engineering do not coincide, but the book does not specifically address what distinguishes technology from engineering and how they are related. In fact, the existence of humanities philosophy of technology and analytic philosophy of technology next to one another reflects a basic ambiguity in the notion of technology that the philosophical work that has been going on has hardly succeeded in clarifying.

Technology can be said to have two aspects or dimensions, which can be referred to as instrumentality and productivity . Instrumentality covers the totality of human endeavours to control their lives and their environments by interfering with the world in an instrumental way, by using things in a purposeful and clever way. Productivity covers the totality of human endeavours to bring into existence new things through which certain things can be realized in a controlled and clever way. For the study of the dimension of instrumentality, it is in principle irrelevant whether the things that are made use of in controlling our lives and environments have been produced by us first; if we somehow could rely on natural objects to always be available to serve our purposes, the analysis of instrumentality and its consequences for how we live our lives would not necessarily be affected. Likewise, for the analysis of what is involved in the making of artifacts, and how the notion of artifact and of something new being brought into existence are to be understood, it is to a large extent irrelevant how human life, culture and society are changed as a result of the artifacts that are in fact produced. Notwithstanding its fundamental character, the ambiguity noted here seems hardly to be confronted directly in the literature. It is addressed by Lawson (2008, 2017) and by Franssen and Koller (2016).

Humanities philosophy of technology has been interested predominantly in the instrumentality dimension, whereas analytic philosophy of technology has focused on the productivity dimension. But technology as one of the basic phenomena of modern society, if not the most basic one, clearly is constituted by the processes centering on and involving both dimensions. It has proved difficult, however, to come to an overarching approach in which the interaction between these two dimensions of technology are adequately dealt with—no doubt partly due to the great differences in philosophical orientation and methodology associated with the two traditions and their separate foci. To improve this situation is arguably the most urgent challenge that the field of philosophy of technology as a whole is facing, since the continuation of the two orientations leading their separate lives threatens its unity and coherence as a discipline in the first place. Indeed, during the past ten to fifteen years the philosophy of engineering has established itself as a subdiscipline within the philosophy of technology, for which a comprehensive handbook was edited recently by Michelfelder and Doorn (2021).

After presenting the major issues of philosophical relevance in technology and engineering that are studied by analytic philosophers of technology in the next section, we discuss the problems and challenges that technology poses for the society in which it is practiced in the third and final section.

2. Analytic Philosophy of Technology

It may come as a surprise to those new to the topic that the fields of philosophy of science and philosophy of technology show such great differences, given that few practices in our society are as closely related as science and engineering. Experimental science is nowadays crucially dependent on technology for the realization of its research set-ups and for gathering and analyzing data. The phenomena that modern science seeks to study could never be discovered without producing them through technology.

Theoretical research within technology has come to be often indistinguishable from theoretical research in science, making engineering science largely continuous with ‘ordinary’ or ‘pure’ science. This is a relatively recent development, which started around the middle of the nineteenth century, and is responsible for great differences between modern technology and traditional, craft-like techniques. The educational training that aspiring scientists and engineers receive starts off being largely identical and only gradually diverges into a science or an engineering curriculum. Ever since the scientific revolution of the seventeenth century, characterized by its two major innovations, the experimental method and the mathematical articulation of scientific theories, philosophical reflection on science has focused on the method by which scientific knowledge is generated, on the reasons for thinking scientific theories to be true, or approximately true, and on the nature of evidence and the reasons for accepting one theory and rejecting another. Hardly ever have philosophers of science posed questions that did not have the community of scientists, their concerns, their aims, their intuitions, their arguments and choices, as a major target. In contrast it is only recently that the philosophy of technology has discovered the community of engineers.

It might be claimed that it is up to the philosophy of technology, and not the philosophy of science, to target first of all the impact of technology—and with it science—on society and culture, because science affects society only through being applied as technology. This, however, will not do. Right from the start of the scientific revolution, science affected human culture and thought fundamentally and directly, not with a detour through technology, and the same is true for later developments such as relativity, atomic physics and quantum mechanics, the theory of evolution, genetics, biochemistry, and the increasingly dominating scientific world view overall. All the same philosophers of science for a long time gave the impression that they left questions addressing the normative, social and cultural aspects of science gladly to other philosophical disciplines, or to historical studies. This has changed only during the past few decades, by scholars either focusing on these issues from the start (e.g. Longino 1990, 2002) or shifting their focus toward them (e.g. Kitcher 2001, 2011).

There is a major difference between the historical development of modern technology as compared to modern science which may at least partly explain this situation, which is that science emerged in the seventeenth century from philosophy itself. The answers that Galileo, Huygens, Newton, and others gave, by which they initiated the alliance of empiricism and mathematical description that is so characteristic of modern science, were answers to questions that had belonged to the core business of philosophy since antiquity. Science, therefore, kept the attention of philosophers. Philosophy of science can be seen as a transformation of epistemology in the light of the emergence of science. The foundational issues—the reality of atoms, the status of causality and probability, questions of space and time, the nature of the quantum world—that were so lively discussed during the end of the nineteenth and the beginning of the twentieth century are an illustration of this close relationship between scientists and philosophers. No such intimacy has ever existed between philosophers and engineers or technologists. Their worlds still barely touch. To be sure, a case can be made that, compared to the continuity existing between natural philosophy and science, a similar continuity exists between central questions in philosophy having to do with human action and practical rationality and the way technology approaches and systematizes the solution of practical problems. To investigate this connection may indeed be considered a major theme for philosophy of technology, and more is said on it in Sections 2.3 and 2.4 . This continuity appears only by hindsight, however, and dimly, as the historical development is at most a slow convening of various strands of philosophical thinking on action and rationality, not a development into variety from a single origin. Significantly it is only the academic outsider Ellul who has, in his idiosyncratic way, recognized in technology the emergent single dominant way of answering all questions concerning human action, comparable to science as the single dominant way of answering all questions concerning human knowledge (Ellul 1954 [1964]). But Ellul was not so much interested in investigating this relationship as in emphasizing and denouncing the social and cultural consequences as he saw them. It is all the more important to point out that humanities philosophy of technology cannot be differentiated from analytic philosophy of technology by claiming that only the former is interested in the social context of technology. There are studies which are rooted in analytic philosophy of science but address in particular the relation of technology to society and culture, and equally the relevance of social relations to technological practices, without taking an evaluative stand with respect to technology; an example is Preston 2012.

The close relationship between the practices of engineering and science may easily keep the important differences between the technology and science from view. The predominant position of science in the philosophical field of vision made it difficult for philosophers to recognize that technology merits special attention for involving issues that do not emerge in science. This view resulting from this lack of recognition is often presented, perhaps somewhat dramatically, as coming down to a claim that technology is ‘merely’ applied science.

A questioning of the relation between science and technology was the central issue in one of the earliest discussions among analytic philosophers of technology. In 1966, in a special issue of the journal Technology and Culture , Henryk Skolimowski argued that technology is something quite different from science (Skolimowski 1966). As he phrased it, science concerns itself with what is, whereas technology concerns itself with what is to be. A few years later, in his well-known book The Sciences of the Artificial (1969), Herbert Simon emphasized this important distinction in almost the same words, stating that the scientist is concerned with how things are but the engineer with how things ought to be. Although it is difficult to imagine that earlier philosophers were blind to this difference in orientation, their inclination, in particular in the tradition of logical empiricism, to view knowledge as a system of statements may have led to a conviction that in technology no knowledge claims play a role that cannot also be found in science. The study of technology, therefore, was not expected to pose new challenges nor hold surprises regarding the interests of analytic philosophy.

In contrast, Mario Bunge (1966) defended the view that technology is applied science, but in a subtle way that does justice to the differences between science and technology. Bunge acknowledges that technology is about action, but an action heavily underpinned by theory—that is what distinguishes technology from the arts and crafts and puts it on a par with science. According to Bunge, theories in technology come in two types: substantive theories, which provide knowledge about the object of action, and operative theories, which are concerned with action itself. The substantive theories of technology are indeed largely applications of scientific theories. The operative theories, in contrast, are not preceded by scientific theories but are born in applied research itself. Still, as Bunge claims, operative theories show a dependence on science in that in such theories the method of science is employed. This includes such features as modeling and idealization, the use of theoretical concepts and abstractions, and the modification of theories by the absorption of empirical data through prediction and retrodiction.

In response to this discussion, Ian Jarvie (1966) proposed as important questions for a philosophy of technology what the epistemological status of technological statements is and how technological statements are to be demarcated from scientific statements. This suggests a thorough investigation of the various forms of knowledge occurring in either practice, in particular, since scientific knowledge has already been so extensively studied, of the forms of knowledge that are characteristic of technology and are lacking, or of much less prominence, in science. A distinction between ‘knowing that’—traditional propositional knowledge—and ‘knowing how’—non-articulated and even impossible-to-articulate knowledge—had been introduced by Gilbert Ryle (1949) in a different context. The notion of ‘knowing how’ was taken up by Michael Polanyi under the name of tacit knowledge and made a central characteristic of technology (Polanyi 1958); the current state of the philosophical discussion is presented in this encyclopedia’s entry on knowledge how . However, emphasizing too much the role of unarticulated knowledge, of ‘rules of thumb’ as they are often called, easily underplays the importance of rational methods in technology. An emphasis on tacit knowledge may also be ill-fit for distinguishing the practices of science and engineering because the role of tacit knowledge in science may well be more important than current philosophy of science acknowledges, for example in concluding causal relationships on the basis of empirical evidence. This was also an important theme in the writings of Thomas Kuhn on theory change in science (Kuhn 1962).

To claim, with Skolimowski and Simon, that technology is about what is to be or what ought to be rather than what is may serve to distinguish it from science but will hardly make it understandable why so much philosophical reflection on technology has taken the form of socio-cultural critique. Technology is an ongoing attempt to bring the world closer to the way one wishes it to be. Whereas science aims to understand the world as it is, technology aims to change the world. These are abstractions, of course. For one, whose wishes concerning what the world should be like are realized in technology? Unlike scientists, who are often personally motivated in their attempts at describing and understanding the world, engineers are seen, not in the least by engineers themselves, as undertaking their attempts to change the world as a service to the public. The ideas on what is to be or what ought to be are seen as originating outside of technology itself; engineers then take it upon themselves to realize these ideas. This view is a major source for the widely spread picture of technology as being instrumental , as delivering instruments ordered from ‘elsewhere’, as means to ends specified outside of engineering, a picture that has served further to support the claim that technology is neutral with respect to values, discussed in Section 3.3.1 . This view involves a considerable distortion of reality, however. Many engineers are intrinsically motivated to change the world, in particular the world as shaped by past technologies. As a result, much technological development is ‘technology-driven’.

To understand where technology ‘comes from’, what drives the innovation process, is of importance not only to those who are curious to understand the phenomenon of technology itself but also to those who are concerned about its role in society. Technology or engineering as a practice is concerned with the creation of artifacts and, of increasing importance, artifact-based services. The design process , the structured process leading toward that goal, forms the core of the practice of engineering. In the engineering literature, the design process is commonly represented as consisting of a series of translational steps; see for this, e.g., Suh 2001. At the start are the customer’s needs or wishes. In the first step these are translated into a list of functional requirements , which then define the design task an engineer, or a team of engineers, has to accomplish. The functional requirements specify as precisely as possible what the device to be designed must be able to do. This step is required because customers usually focus on just one or two features and are unable to articulate the requirements that are necessary to support the functionality they desire. In the second step, the functional requirements are translated into design specifications , which the exact physical parameters of crucial components by which the functional requirements are going to be met. The design parameters chosen to satisfy these requirements are combined and made more precise such that a blueprint of the device results. The blueprint contains all the details that must be known such that the final step to the process of manufacturing the device can take place. It is tempting to consider the blueprint as the end result of a design process, instead of a finished copy being this result. However, actual copies of a device are crucial for the purpose of prototyping and testing. Prototyping and testing presuppose that the sequence of steps making up the design process can and will often contain iterations, leading to revisions of the design parameters and/or the functional requirements. Even though, certainly for mass-produced items, the manufacture of a product for delivery to its customers or to the market comes after the closure of the design phase, the manufacturing process is often reflected in the functional requirements of a device, for example in putting restrictions on the number of different components of which the device consists. The complexity of a device will affect how difficult it will be to maintain or repair it, and ease of maintenance or low repair costs are often functional requirements. An important modern development is that the complete life cycle of an artifact is now considered to be the designing engineer’s concern, up till the final stages of the recycling and disposal of its components and materials, and the functional requirements of any device should reflect this. From this point of view, neither a blueprint nor a prototype can be considered the end product of engineering design.

The biggest idealization that this scheme of the design process contains is arguably located at the start. Only in a minority of cases does a design task originate in a customer need or wish for a particular artifact. First of all, as already suggested, many design tasks are defined by engineers themselves, for instance, by noticing something to be improved in existing products. Nevertheless design often starts with a problem pointed out by some societal agent, which engineers are then invited to solve. Many such problems, however, are ill-defined or wicked problems, meaning that it is not at all clear what the problem is exactly and what a solution to the problem would consist in. The ‘problem’ is a situation that people—not necessarily the people ‘in’ the situation—find unsatisfactory, but typically without being able to specify a situation that they find more satisfactory in other terms than as one in which the problem has been solved. In particular it is not obvious that a solution to the problem would consist in some artifact, or some artifactual system or process, being made available or installed. Engineering departments all over the world advertise that engineering is problem solving, and engineers easily seem confident that they are best qualified to solve a problem when they are asked to, whatever the nature of the problem. This has led to the phenomenon of a technological fix , the solution of a problem by a technical solution, that is, the delivery of an artifact or artifactual process, where it is questionable, to say the least, whether this solves the problem or whether it was the best way of handling the problem.

A candidate example of a technological fix for the problem of global warming would be the currently much debated option of injecting sulfate aerosols into the stratosphere to offset the warming effect of greenhouse gases such as carbon dioxide and methane. Such schemes of geoengineering would allow us to avoid facing the—in all likelihood painful—choices that will lead to a reduction of the emission of greenhouse gases into the atmosphere, but will at the same time allow the depletion of the Earth’s reservoir of fossil fuels to continue. See for a discussion of technological fixing, e.g., Volti 2009: 26–32. Given this situation, and its hazards, the notion of a problem and a taxonomy of problems deserve to receive more philosophical attention than they have hitherto received.

These wicked problems are often broadly social problems, which would best be met by some form of ‘social action’, which would result in people changing their behavior or acting differently in such a way that the problem would be mitigated or even disappear completely. In defense of the engineering view, it could perhaps be said that the repertoire of ‘proven’ forms of social action is meager. The temptation of technical fixes could be overcome—at least that is how an engineer might see it—by the inclusion of the social sciences in the systematic development and application of knowledge to the solution of human problems. This however, is a controversial view. Social engineering is to many a specter to be kept at as large a distance as possible instead of an ideal to be pursued. Karl Popper referred to acceptable forms of implementing social change as ‘piecemeal social engineering’ and contrasted it to the revolutionary but completely unfounded schemes advocated by, e.g., Marxism. In the entry on Karl Popper , however, his choice of words is called ‘rather unfortunate’. The notion of social engineering, and its cogency, deserves more attention that it is currently receiving.

An important input for the design process is scientific knowledge: knowledge about the behavior of components and the materials they are composed of in specific circumstances. This is the point where science is applied. However, much of this knowledge is not directly available from the sciences, since it often concerns extremely detailed behavior in very specific circumstances. This scientific knowledge is therefore often generated within technology, by the engineering sciences. But apart from this very specific scientific knowledge, engineering design involves various other sorts of knowledge. In his book What Engineers Know and How They Know It (Vincenti 1990), the aeronautical engineer Walter Vincenti gave a six-fold categorization of engineering design knowledge (leaving aside production and operation as the other two basic constituents of engineering practice). Vincenti distinguishes

• Fundamental design concepts, including primarily the operational principle and the normal configuration of a particular device;
• Criteria and specifications;
• Theoretical tools;
• Quantitative data;
• Practical considerations;
• Design instrumentalities.

The fourth category concerns the quantitative knowledge just referred to, and the third the theoretical tools used to acquire it. These two categories can be assumed to match Bunge’s notion of substantive technological theories. The status of the remaining four categories is much less clear, however, partly because they are less familiar, or not at all, from the well-explored context of science. Of these categories, Vincenti claims that they represent prescriptive forms of knowledge rather than descriptive ones. Here, the activity of design introduces an element of normativity, which is absent from scientific knowledge. Take such a basic notion as ‘operational principle’, which refers to the way in which the function of a device is realized, or, in short, how it works. This is still a purely descriptive notion. Subsequently, however, it plays a role in arguments that seek to prescribe a course of action to someone who has a goal that could be realized by the operation of such a device. At this stage, the issue changes from a descriptive to a prescriptive or normative one. An extensive discussion of the various kinds of knowledge relevant to technology is offered by Houkes (2009).

Although the notion of an operational principle—a term that seems to originate with Polanyi (1958)—is central to engineering design, no single clear-cut definition of it seems to exist. The issue of disentangling descriptive from prescriptive aspects in an analysis of technical actions and their constituents is therefore a task that has hardly begun. This task requires a clear view on the extent and scope of technology. If one follows Joseph Pitt in his book Thinking About Technology (1999) and defines technology broadly as ‘humanity at work’, then to distinguish between technical action and action in general becomes difficult, and the study of action in technology must absorb all descriptive and normative theories of action, including the theory of practical rationality, and much of theoretical economics in its wake. There have indeed been attempts at such an encompassing account of human action, for example Tadeusz Kotarbinski’s Praxiology (1965), but a perspective of such generality makes it difficult to arrive at results of sufficient depth. It would be a challenge for philosophy to specify the differences among action forms and the reasoning grounding them in, to single out three prominent fields of study, technology, organization and management, and economics.

A more restricted attempt at such an approach is Ilkka Niiniluoto’s (1993). According to Niiniluoto, the theoretical framework of technology as an activity that is concerned with what the world should be like rather than is, the framework that forms the counterpoint to the descriptive framework of science, is design science . The content of design science, the counterpoint to the theories and explanations that form the content of descriptive science, would then be formed by technical norms , statements of the form ‘If one wants to achieve X , one should do Y ’. The notion of a technical norm derives from Georg Henrik von Wright’s Norm and Action (1963). Technical norms need to be distinguished from anankastic statements expressing natural necessity, of the form ‘If X is to be achieved, Y needs to be done’; the latter have a truth value but the former have not. Von Wright himself, however, wrote that he did not understand the mutual relations between these statements. Zwart, Franssen and Kroes (2018) present a detailed discussion. Ideas on what design science is and can and should be are evidently related to the broad problem area of practical rationality—see this encyclopedia’s entries on practical reason and instrumental rationality —and also to means-ends reasoning, discussed in the next section.

Design is an activity that is subject to rational scrutiny but in which creativity is considered to play an important role as well. Since design is a form of action, a structured series of decisions to proceed in one way rather than another, the form of rationality that is relevant to it is practical rationality, the rationality incorporating the criteria on how to act, given particular circumstances. This suggests a clear division of labor between the part to be played by rational scrutiny and the part to be played by creativity. Theories of rational action generally conceive their problem situation as one involving a choice among various course of action open to the agent. Rationality then concerns the question how to decide among given options, whereas creativity concerns the generation of these options. This distinction is similar to the distinction between the context of justification and the context of discovery in science. The suggestion that is associated with this distinction, however, that rational scrutiny only applies in the context of justification, is difficult to uphold for technological design. If the initial creative phase of option generation is conducted sloppily, the result of the design task can hardly be satisfactory. Unlike the case of science, where the practical consequences of entertaining a particular theory are not taken into consideration, the context of discovery in technology is governed by severe constraints of time and money, and an analysis of the problem how best to proceed certainly seems in order. There has been little philosophical work done in this direction; an overview of the issues is given in Kroes, Franssen, and Bucciarelli (2009).

The ideas of Herbert Simon on bounded rationality (see, e.g., Simon 1982) are relevant here, since decisions on when to stop generating options and when to stop gathering information about these options and the consequences when they are adopted are crucial in decision making if informational overload and calculative intractability are to be avoided. However, it has proved difficult to further develop Simon’s ideas on bounded rationality since their conception in the 1950s. Another notion that is relevant here is means-ends reasoning. In order to be of any help here, theories of means-ends reasoning should then concern not just the evaluation of given means with respect to their ability to achieve given ends, but also the generation or construction of means for given ends. A comprehensive theory of means-ends reasoning, however, is not yet available; for a proposal on how to develop means-ends reasoning in the context of technical artifacts, see Hughes, Kroes, and Zwart 2007. In the practice of engineering, alternative proposals for the realization of particular functions are usually taken from ‘catalogs’ of existing and proven realizations. These catalogs are extended by ongoing research in technology rather than under the urge of particular design tasks.

When engineering design is conceived as a process of decision making, governed by considerations of practical rationality, the next step is to specify these considerations. Almost all theories of practical rationality conceive of it as a reasoning process where a match between beliefs and desires or goals is sought. The desires or goals are represented by their value or utility for the decision maker, and the decision maker’s problem is to choose an action that realizes a situation that, ideally, has maximal value or utility among all the situations that could be realized. If there is uncertainty concerning the situations that will be realized by a particular action, then the problem is conceived as aiming for maximal expected value or utility. Now the instrumental perspective on technology implies that the value that is at issue in the design process viewed as a process of rational decision making is not the value of the artifacts that are created. Those values are the domain of the users of the technology so created. They are supposed to be represented in the functional requirements defining the design task. Instead the value to be maximized is the extent to which a particular design meets the functional requirements defining the design task. It is in this sense that engineers share an overall perspective on engineering design as an exercise in optimization . But although optimization is a value-orientated notion, it is not itself perceived as a value driving engineering design.

The functional requirements that define most design problems do not prescribe explicitly what should be optimized; usually they set levels to be attained minimally. It is then up to the engineer to choose how far to go beyond meeting the requirements in this minimal sense. Efficiency , in energy consumption and use of materials first of all, is then often a prime value. Under the pressure of society, other values have come to be incorporated, in particular safety and, more recently, sustainability . Sometimes it is claimed that what engineers aim to maximize is just one factor, namely market success. Market success, however, can only be assessed after the fact. The engineer’s maximization effort will instead be directed at what are considered the predictors of market success. Meeting the functional requirements and being relatively efficient and safe are plausible candidates as such predictors, but additional methods, informed by market research, may introduce additional factors or may lead to a hierarchy among the factors.

Choosing the design option that maximally meets all the functional requirements (which may but need not originate with the prospective user) and all other considerations and criteria that are taken to be relevant, then becomes the practical decision-making problem to be solved in a particular engineering-design task. This creates several methodological problems. Most important of these is that the engineer is facing a multi-criteria decision problem. The various requirements come with their own operationalizations in terms of design parameters and measurement procedures for assessing their performance. This results in a number of rank orders or quantitative scales which represent the various options out of which a choice is to be made. The task is to come up with a final score in which all these results are ‘adequately’ represented, such that the option that scores best can be considered the optimal solution to the design problem. Engineers describe this situation as one where trade-offs have to be made: in judging the merit of one option relative to other options, a relative bad performance on one criterion can be balanced by a relatively good performance on another criterion. An important problem is whether a rational method for doing this can be formulated. It has been argued by Franssen (2005) that this problem is structurally similar to the well-known problem of social choice, for which Kenneth Arrow proved his notorious impossibility theorem in 1950. As a consequence, as long as we require from a solution method to this problem that it answers to some requirements that spell out its generality and rationality, no such solution method exists. In technical design, the role that individual voters play in situations of social choice is played by the various design criteria, which each have a say in what the resulting product comes to look like. This poses serious problems for the claims of engineers that their designs are optimal solutions in the sense of satisfying the totality of the design criteria best, since Arrow’s theorem implies that in most multi-criteria problems this notion of ‘optimal’ cannot be rigorously defined, just as in most multi-voter situations the notion of a best or even adequate representation of what the voters jointly want cannot be rigorously defined.

This result seems to except a crucial aspect of engineering activity from philosophical scrutiny, and it could be used to defend the opinion that engineering is at least partly an art, not a science. Instead of surrendering to the result, however, which has a significance that extends much beyond engineering and even beyond decision making in general, we should perhaps conclude instead that there is still a lot of work to be done on what might be termed, provisionally, ‘approximative’ forms of reasoning. One form of reasoning to be included here is Herbert Simon’s bounded rationality, plus the related notion of ‘satisficing’. Since their introduction in the 1950s (Simon 1957) these two terms have found wide usage, but we are still lacking a general theory of bounded rationality. It may be in the nature of forms of approximative reasoning such as bounded rationality that a general theory cannot be had, but even a systematic treatment from which such an insight could emerge seems to be lacking.

Another problem for the decision-making view of engineering design is that in modern technology almost all design is done by teams. Such teams are composed of experts from many different disciplines. Each discipline has its own theories, its own models of interdependencies, its own assessment criteria, and so forth, and the professionals belonging to these disciplines must be considered as inhabitants of different object worlds , as Louis Bucciarelli (1994) phrases it. The different team members are, therefore, likely to disagree on the relative rankings and evaluations of the various design options under discussion. Agreement on one option as the overall best one can here be even less arrived at by an algorithmic method exemplifying engineering rationality. Instead, models of social interaction, such as bargaining and strategic thinking, are relevant here. An example of such an approach to an (abstract) design problem is presented by Franssen and Bucciarelli (2004).

To look in this way at technological design as a decision-making process is to view it normatively from the point of view of practical or instrumental rationality. At the same time it is descriptive in that it is a description of how engineering methodology generally presents the issue how to solve design problems. From that somewhat higher perspective there is room for all kinds of normative questions that are not addressed here, such as whether the functional requirements defining a design problem can be seen as an adequate representation of the values of the prospective users of an artifact or a technology, or by which methods values such as safety and sustainability can best be elicited and represented in the design process. These issues will be taken up in Section 3 .

Understanding the process of designing artifacts is the theme in philosophy of technology that most directly touches on the interests of engineering practice. This is hardly true for another issue of central concern to analytic philosophy of technology, which is the status and the character of artifacts. This is perhaps not unlike the situation in the philosophy of science, where working scientists seem also to be much less interested in investigating the status and character of models and theories than philosophers are.

Artifacts are man-made objects: they have an author (see Hilpinen 1992 and Hilpinen’s article artifact in this encyclopedia). The artifacts that are of relevance to technology are, additionally, made to serve a purpose. This excludes, within the set of all man-made objects, byproducts and waste products and equally, though controversially, works of art. Byproducts and waste products result from an intentional act to make something but just not precisely, although the author at work may be well aware of their creation. Works of art result from an intention directed at their creation (although in exceptional cases of conceptual art, this directedness may involve many intermediate steps) but it is contested whether artists include in their intentions concerning their work an intention that the work serves some purpose. Nevertheless, most philosophers of technology who discuss the metaphysics of artifacts exclude artworks from their analyses. A further discussion of this aspect belongs to the philosophy of art. An interesting general account which does not do so has been presented by Dipert (1993).

Technical artifacts, then, are made to serve some purpose, generally to be used for something or to act as a component in a larger artifact, which in its turn is either something to be used or again a component. Whether end product or component, an artifact is ‘for something’, and what it is for is called the artifact’s function . Several researchers have emphasized that an adequate description of artifacts must refer both to their status as tangible physical objects and to the intentions of the people engaged with them. Kroes and Meijers (2006) have dubbed this view “the dual nature of technical artifacts”; its most mature formulation is Kroes 2012. They suggest that the two aspects are ‘tied up’, so to speak, in the notion of artifact function. This gives rise to several problems. One, which will be passed over quickly because little philosophical work seems to have been done concerning it, is that structure and function mutually constrain each other, but the constraining is only partial. It is unclear whether a general account of this relation is possible and what problems need to be solved to arrive there. There may be interesting connections with the issue of multiple realizability in the philosophy of mind and with accounts of reduction in science; an example where this is explored is Mahner and Bunge 2001.

It is equally problematic whether a unified account of the notion of function as such is possible, but this issue has received considerably more philosophical attention. The notion of function is of paramount importance for characterizing artifacts, but the notion is used much more widely. The notion of an artifact’s function seems to refer necessarily to human intentions. Function is also a key concept in biology, however, where no intentionality plays a role, and it is a key concept in cognitive science and the philosophy of mind, where it is crucial in grounding intentionality in non-intentional, structural and physical properties. Up till now there is no accepted general account of function that covers both the intentionality-based notion of artifact function and the non-intentional notion of biological function—not to speak of other areas where the concept plays a role, such as the social sciences. The most comprehensive theory, that has the ambition to account for the biological notion, cognitive notion and the intentional notion, is Ruth Millikan’s 1984; for criticisms and replies, see Preston 1998, 2003; Millikan 1999; Vermaas & Houkes 2003; and Houkes & Vermaas 2010. The collection of essays edited by Ariew, Cummins and Perlman (2002) presents an introduction to the topic of characterizing the notion of function, although the emphasis is on biological functions. This emphasis remains very strong in the literature, as can be judged from the most recent critical overview (Garson 2016), which explicitly refrains from discussing artifact functions.

Against the view that, at least in the case of artifacts, the notion of function refers necessarily to intentionality, it could be argued that in discussing the functions of the components of a larger device, and the interrelations between these functions, the intentional ‘side’ of these functions is of secondary importance only. This, however, would be to ignore the possibility of the malfunctioning of such components. This notion seems to be definable only in terms of a mismatch between actual behavior and intended behavior. The notion of malfunction also sharpens an ambiguity in the general reference to intentions when characterizing technical artifacts. These artifacts usually engage many people, and the intentions of these people may not all pull in the same direction. A major distinction can be drawn between the intentions of the actual user of an artifact for a particular purpose and the intentions of the artifact’s designer. Since an artifact may be used for a purpose different from the one for which its designer intended it to be used, and since people may also use natural objects for some purpose or other, one is invited to allow that artifacts can have multiple functions, or to enforce a hierarchy among all relevant intentions in determining the function of an artifact, or to introduce a classification of functions in terms of the sorts of determining intentions. In the latter case, which is a sort of middle way between the two other options, one commonly distinguishes between the proper function of an artifact as the one intended by its designer and the accidental function of the artifact as the one given to it by some user on private considerations. Accidental use can become so common, however, that the original function drops out of memory.

Closely related to this issue to what extent use and design determine the function of an artifact is the problem of characterizing artifact kinds. It may seem that we use functions to classify artifacts: an object is a knife because it has the function of cutting, or more precisely, of enabling us to cut. On closer inspection, however, the link between function and kind-membership seems much less straightforward. The basic kinds in technology are, for example, ‘knife’, ‘aircraft’ and ‘piston’. The members of these kinds have been designed in order to be used to cut something with, to transport something through the air and to generate mechanical movement through thermodynamic expansion, respectively. However, one cannot create a particular kind of artifact just by designing something with the intention that it be used for some particular purpose: a member of the kind so created must actually be useful for that purpose. Despite innumerable design attempts and claims, the perpetual motion machine is not a kind of artifact. A kind like ‘knife’ is defined, therefore, not only by the intentions of the designers of its members that they each be useful for cutting but also by a shared operational principle known to these designers, and on which they based their design. This is, in a different setting, also defended by Thomasson, who in her characterization of what she in general calls an artifactual kind says that such a kind is defined by the designer’s intention to make something of that kind, by a substantive idea that the designer has of how this can be achieved, and by his or her largely successful achievement of it (Thomasson 2003, 2007). Qua sorts of kinds in which artifacts can be grouped, a distinction must therefore be made between a kind like ‘knife’ and a corresponding but different kind ‘cutter’. A ‘knife’ indicates a particular way a ‘cutter’ can be made. One can also cut, however, with a thread or line, a welding torch, a water jet, and undoubtedly by other sorts of means that have not yet been thought of. A ‘cutter’ would then refer to a truly functional kind. As such, it is subject to the conflict between use and design: one could mean by ‘cutter’ anything than can be used for cutting or anything that has been designed to be used for cutting, by the application of whatever operational principle, presently known or unknown.

This distinction between artifact kinds and functional kinds is relevant for the status of such kinds in comparison to other notions of kinds. Philosophy of science has emphasized that the concept of natural kind, such as exemplified by ‘water’ or ‘atom’, lies at the basis of science. On the other hand it is generally taken for granted that there are no regularities that all knives or airplanes or pistons answer to. This, however, is loosely based on considerations of multiple realizability that fully apply only to functional kinds, not to artifact kinds. Artifact kinds share an operational principle that gives them some commonality in physical features, and this commonality becomes stronger once a particular artifact kind is subdivided into narrower kinds. Since these kinds are specified in terms of physical and geometrical parameters, they are much closer to the natural kinds of science, in that they support law-like regularities; see for a defense of this position (Soavi 2009). A recent collection of essays that discuss the metaphysics of artifacts and artifact kinds is Franssen, Kroes, Reydon and Vermaas 2014.

There is at least one additional technology-related topic that ought to be mentioned because it has created a good deal of analytic philosophical literature, namely Artificial Intelligence and related areas. A full discussion of this vast field is beyond the scope of this entry, however. Information is to be found in the entries on Turing machines , the Church-Turing thesis , computability and complexity , the Turing test , the Chinese room argument , the computational theory of mind , functionalism , multiple realizability , and the philosophy of computer science .

3. Ethical and Social Aspects of Technology

It was not until the twentieth century that the development of the ethics of technology as a systematic and more or less independent subdiscipline of philosophy started. This late development may seem surprising given the large impact that technology has had on society, especially since the industrial revolution.

A plausible reason for this late development of ethics of technology is the instrumental perspective on technology that was mentioned in Section 2.2 . This perspective implies, basically, a positive ethical assessment of technology: technology increases the possibilities and capabilities of humans, which seems in general desirable. Of course, since antiquity, it has been recognized that the new capabilities may be put to bad use or lead to human hubris . Often, however, these undesirable consequences are attributed to the users of technology, rather than the technology itself, or its developers. This vision is known as the instrumental vision of technology resulting in the so-called neutrality thesis. The neutrality thesis holds that technology is a neutral instrument that can be put to good or bad use by its users. During the twentieth century, this neutrality thesis met with severe critique, most prominently by Heidegger and Ellul, who have been mentioned in this context in Section 2 , but also by philosophers from the Frankfurt School, such as Horkheimer and Adorno (1947 [2002]), Marcuse (1964), and Habermas (1968 [1970]).

The scope and the agenda for ethics of technology to a large extent depend on how technology is conceptualized. The second half of the twentieth century has witnessed a richer variety of conceptualizations of technology that move beyond the conceptualization of technology as a neutral tool, as a world view or as a historical necessity. This includes conceptualizations of technology as a political phenomenon (Winner, Feenberg, Sclove), as a social activity (Latour, Callon, Bijker and others in the area of science and technology studies), as a cultural phenomenon (Ihde, Borgmann), as a professional activity (engineering ethics, e.g., Davis), and as a cognitive activity (Bunge, Vincenti). Despite this diversity, the development in the second half of the twentieth century is characterized by two general trends. One is a move away from technological determinism and the assumption that technology is a given self-contained phenomenon which develops autonomously to an emphasis on technological development being the result of choices (although not necessarily the intended result). The other is a move away from ethical reflection on technology as such to ethical reflection of specific technologies and to specific phases in the development of technology. Both trends together have resulted in an enormous increase in the number and scope of ethical questions that are asked about technology. The developments also imply that ethics of technology is to be adequately empirically informed, not only about the exact consequences of specific technologies but also about the actions of engineers and the process of technological development. This has also opened the way to the involvement of other disciplines in ethical reflections on technology, such as Science and Technology Studies (STS) and Technology Assessment (TA).

3.2 Approaches in the Ethics of Technology

Not only is the ethics of technology characterized by a diversity of approaches, it might even be doubted whether something like a subdiscipline of ethics of technology, in the sense of a community of scholars working on a common set of problems, exists. The scholars studying ethical issues in technology have diverse backgrounds (e.g., philosophy, STS, TA, law, political science, and STEM disciplines) and they do not always consider themselves (primarily) ethicists of technology. To give the reader an overview of the field, three basic approaches or strands that might be distinguished in the ethics of technology will be discussed.

Both cultural and political approaches build on the traditional philosophy and ethics of technology of the first half of the twentieth century. Whereas cultural approaches conceive of technology as a cultural phenomenon that influences our perception of the world, political approaches conceive of technology as a political phenomenon, i.e., as a phenomenon that is ruled by and embodies institutional power relations between people.

Cultural approaches are often phenomenological in nature or at least position themselves in relation to phenomenology as post-phenomenology. Examples of philosophers in this tradition are Don Ihde, Albert Borgmann, Peter-Paul Verbeek and Evan Selinger (e.g., Borgmann 1984; Ihde 1990; Verbeek 2000 [2005], 2011). The approaches are usually influenced by developments in STS, especially the idea that technologies contain a script that influences not only people’s perception of the world but also human behavior, and the idea of the absence of a fundamental distinction between humans and non-humans, including technological artifacts (Akrich 1992; Latour 1992, 1993; Ihde & Selinger 2003). The combination of both ideas has led some to claim that technology has (moral) agency, a claim that is discussed below in Section 3.3.1 .

Political approaches to technology mostly go back to Marx, who assumed that the material structure of production in society, in which technology is obviously a major factor, determined the economic and social structure of that society. Similarly, Langdon Winner has argued that technologies can embody specific forms of power and authority (Winner 1980). According to him, some technologies are inherently normative in the sense that they require or are strongly compatible with certain social and political relations. Railroads, for example, seem to require a certain authoritative management structure. In other cases, technologies may be political due to the particular way they have been designed. Some political approaches to technology are inspired by (American) pragmatism and, to a lesser extent, discourse ethics. A number of philosophers, for example, have pleaded for a democratization of technological development and the inclusion of ordinary people in the shaping of technology (Winner 1983; Sclove 1995; Feenberg 1999). Such ideas are also echoed in recent interdisciplinary approaches, such as Responsible Research and Innovation (RRI), that aim at opening up the innovation process to a broader range of stakeholders and concerns (Owen et al. 2013).

Although political approaches have obviously ethical ramifications, many philosophers who initially adopted such approaches do not engage in explicit ethical reflection. Also in political philosophy, technology does not seem to have been taken up as an important topic. Nevertheless, particularly in relation to digital technologies such as social media, algorithms and more generally Artificial Intelligence (AI), a range of political themes has recently been discussed, such as threats to democracy (from e.g. social media), the power of Big Tech companies, and new forms of exploitation, domination and colonialism that may come with AI (e.g., Coeckelbergh 2022; Susskind 2022; Zuboff 2017; Adams 2021). An important emerging theme is also justice, which does not just encompass distributive justice (Rawls 1999), but also recognition justice (Fraser and Honneth 2003) and procedural justice. Questions about justice have not only been raised by digital technologies, but also by climate change and energy technologies, leading to the coinage of new notions like climate justice (Caney 2014) and energy justice (Jenkins et al. 2016).

Engineering ethics started off in the 1980s in the United States, merely as an educational effort. Engineering ethics is concerned with “the actions and decisions made by persons, individually or collectively, who belong to the profession of engineering” (Baum 1980: 1). According to this approach, engineering is a profession, in the same way as medicine is a profession.

Although there is no agreement on how a profession exactly should be defined, the following characteristics are often mentioned:

• A profession relies on specialized knowledge and skills that require a long period of study;
• The occupational group has a monopoly on the carrying out of the occupation;
• The assessment of whether the professional work is carried out in a competent way is done by, and it is accepted that this can only be done by, professional peers;
• A profession provides society with products, services or values that are useful or worthwhile for society, and is characterized by an ideal of serving society;
• The daily practice of professional work is regulated by ethical standards, which are derived from or relate to the society-serving ideal of the profession.

Typical ethical issues that are discussed in engineering ethics are professional obligations of engineers as exemplified in, for example, codes of ethics of engineers, the role of engineers versus managers, competence, honesty, whistle-blowing, concern for safety and conflicts of interest (Davis 1998, 2005). Over the years, the scope of engineering ethics has been broadened. Whereas it initially often focused on decisions of individual engineers and on questions like whistle-blowing and loyalty, textbooks now also discuss the wider context in which such decisions are made and pay attention to for example, the so-called problem of many hands (van de Poel and Royakkers 2011; Peterson 2020) (see also section 3.3.2). Initially, the focus was often primarily on safety concerns and issues like competence and conflicts of interests, but nowadays also issues of sustainability, social justice, privacy, global issues and the role of technology in society are discussed (Harris, Pritchard, and Rabins 2014; Martin and Schinzinger 2022; Taebi 2021; Peterson 2020; van de Poel and Royakkers 2011).

The last decades have witnessed an enormous increase in ethical inquiries into specific technologies. This may now be the largest of the three strands discussed, especially given the rapid growth in technology-specific ethical inquiries in the last two decades. One of the most visible new fields nowadays is digital ethics, which evolved from computer ethics (e.g., Moor 1985; Floridi 2010; Johnson 2009; Weckert 2007; van den Hoven & Weckert 2008), with more recently a focus on robotics, artificial intelligence, machine ethics, and the ethics of algorithms (Lin, Abney, & Jenkins 2017; Nucci & Santoni de Sio 2016; Mittelstadt et al. 2016; Bostrom & Yudkowsky 2014; Wallach & Allen 2009, Coeckelbergh 2020b). Other technologies like biotechnology have also spurred dedicated ethical investigations (e.g., Sherlock & Morrey 2002; P. Thompson 2007). More traditional fields like architecture and urban planning have also attracted specific ethical attention (Fox 2000). Nanotechnology and so-called converging technologies have led to the establishment of what is called nanoethics (Allhoff et al. 2007). Other examples are the ethics of nuclear deterrence (Finnis et al. 1988), nuclear energy (Taebi & Roeser 2015) and geoengineering (Christopher Preston 2016).

Obviously the establishment of such new fields of ethical reflection is a response to social and technological developments. Still, the question can be asked whether the social demand is best met by establishing new fields of applied ethics. This issue is in fact regularly discussed as new fields emerge. Several authors have for example argued that there is no need for nanoethics because nanotechnology does not raise any really new ethical issues (e.g., McGinn 2010). The alleged absence of newness here is supported by the claim that the ethical issues raised by nanotechnology are a variation on, and sometimes an intensification of, existing ethical issues, but hardly really new, and by the claim that these issues can be dealt with the existing theories and concepts from moral philosophy. For an earlier, similar discussion concerning the supposed new character of ethical issues in computer engineering, see Tavani 2002.

The new fields of ethical reflection are often characterized as applied ethics, that is, as applications of theories, normative standards, concepts and methods developed in moral philosophy. For each of these elements, however, application is usually not straightforward but requires a further specification or revision. This is the case because general moral standards, concepts and methods are often not specific enough to be applicable in any direct sense to specific moral problems. ‘Application’ therefore often leads to new insights which might well result in the reformulation or at least refinement of existing normative standards, concepts and methods. In some cases, ethical issues in a specific field might require new standards, concepts or methods. Beauchamp and Childress for example have proposed a number of general ethical principles for biomedical ethics (Beauchamp & Childress 2001). These principles are more specific than general normative standards, but still so general and abstract that they apply to different issues in biomedical ethics. In computer ethics, existing moral concepts relating to for example privacy and ownership has been redefined and adapted to deal with issues which are typical for the computer age (Johnson 2003). An example is Nissenbaum’s proposal to understand privacy in terms of contextual integrity (Nissenbaum 2010). New fields of ethical application might also require new methods for, for example, discerning ethical issues that take into account relevant empirical facts about these fields, like the fact that technological research and development usually takes place in networks of people rather than by individuals (Zwart et al. 2006). Another more general issue that applies to many new technologies is how to deal with the uncertainties about (potential) social and ethical impacts that typically surround new emerging technologies. Brey’s (2012) proposal for an anticipatory ethics may be seen as a reply to this challenge. The issue of anticipation is also one of the central concerns in the more recent interdisciplinary field of Responsible Research and Innovation (RRI) (e.g., Owen et al. 2013).

Although different fields of ethical reflection on specific technologies might well raise their own philosophical and ethical issues, it can be questioned whether this justifies the development of separate subfields or even subdisciplines. One obvious argument might be that in order to say something ethically meaningful about new technologies, one needs specialized and detailed knowledge of a specific technology. Moreover such subfields allow interaction with relevant non-philosophical experts in for example law, psychology, economy, science and technology studies (STS) or technology assessment (TA), as well as the relevant STEM (Science, Technology, Engineering, Medicine) disciplines. On the other side, it could also be argued that a lot can be learned from interaction and discussion between ethicists specializing in different technologies, and a fruitful interaction with the two other strands discussed above (cultural and political approaches and engineering ethics). In particular more political approaches to technology can be complementary to approaches that focus on ethical issue of specific technologies (such as AI) by drawing attention to justice issues, power differences and the role of larger institutional and international contexts. Currently, such interaction in many cases seems absent, although there are of course exceptions.

3.3 Some Recurrent Themes in the Ethics of Technology

We now turn to the description of some specific themes in the ethics of technology. We focus on a number of general themes that provide an illustration of general issues in the ethics of technology and the way these are treated.

One important general theme in the ethics of technology is the question whether technology is value-laden. Some authors have maintained that technology is value-neutral, in the sense that technology is just a neutral means to an end, and accordingly can be put to good or bad use (e.g., Pitt 2000). This view might have some plausibility in as far as technology is considered to be just a bare physical structure. Most philosophers of technology, however, agree that technological development is a goal-oriented process and that technological artifacts by definition have certain functions, so that they can be used for certain goals but not, or far more difficulty or less effectively, for other goals. This conceptual connection between technological artifacts, functions and goals makes it hard to maintain that technology is value-neutral. Even if this point is granted, the value-ladenness of technology can be construed in a host of different ways. Some authors have maintained that technology can have moral agency. This claim suggests that technologies can autonomously and freely ‘act’ in a moral sense and can be held morally responsible for their actions.

The debate whether technologies can have moral agency started off in computer ethics (Bechtel 1985; Snapper 1985; Dennett 1997; Floridi & Sanders 2004) but has since broadened. Typically, the authors who claim that technologies (can) have moral agency often redefine the notion of agency or its connection to human will and freedom (e.g., Latour 1993; Floridi & Sanders 2004, Verbeek 2011). A disadvantage of this strategy is that it tends to blur the morally relevant distinctions between people and technological artifacts. More generally, the claim that technologies have moral agency sometimes seems to have become shorthand for claiming that technology is morally relevant. This, however, overlooks the fact technologies can be value-laden in other ways than by having moral agency (see, e.g., Johnson 2006; Radder 2009; Illies & Meijers 2009; Peterson & Spahn 2011; Miller 2020; Klenk 2021). One might, for example, claim that technology enables (or even invites) and constrains (or even inhibits) certain human actions and the attainment of certain human goals and therefore is to some extent value-laden, without claiming moral agency for technological artifacts. A good overview of the debate can be found in Kroes and Verbeek 2014.

The debate about moral agency and technology is now particularly salient with respect to the design of intelligent artificial agents. James Moor (2006) has distinguished between four ways in which artificial agents may be or become moral agents:

• Ethical impact agents are robots and computer systems that ethically impact their environment; this is probably true of all artificial agents.
• Implicit ethical agents are artificial agents that have been programmed to act according to certain values.
• Explicit ethical agents are machines that can represent ethical categories and that can ‘reason’ (in machine language) about these.
• Full ethical agents in addition also possess some characteristics we often consider crucial for human agency, like consciousness, free will and intentionality.

It might perhaps never be possible to technologically design full ethical agents, and if it were to become possible it might be questionable whether it is morally desirable to do so (Bostrom & Yudkowsky 2014; van Wynsberghe and Robbins 2019). As Wallach and Allen (2009) have pointed out, the main problem might not be to design artificial agents that can function autonomously and that can adapt themselves in interaction with the environment, but rather to build enough, and the right kind of, ethical sensitivity into such machines.

Apart from the question whether intelligent artificial agents can have moral agency, there are (broader) questions about their moral status; for example would they—and if so under what conditions—qualify as moral patients, to which humans have certain moral obligations. Traditionally, moral status is connected to consciousness, but a number of authors have proposed more minimal criteria for moral status, particularly for (social) robots. For example, Danaher (2020) has suggested that behaviouristic criteria might suffice whereas Coeckelbergh (2014) and Gunkel (2018) have suggested a relational approach. Mosakas (2021) has argued that such approaches do not ground moral status, and hence humans have no direct moral duties towards social robots (although they may still be morally relevant in other ways). Others have suggested that social robots sometimes may deceive us into believing they have certain cognitive and emotional capabilities (that may give them also moral status) while they have not (Sharkey and Sharkey 2021).

Responsibility has always been a central theme in the ethics of technology. The traditional philosophy and ethics of technology, however, tended to discuss responsibility in rather general terms and were rather pessimistic about the possibility of engineers to assume responsibility for the technologies they developed. Ellul, for example, has characterized engineers as the high priests of technology, who cherish technology but cannot steer it. Hans Jonas (1979 [1984]) has argued that technology requires an ethics in which responsibility is the central imperative because for the first time in history we are able to destroy the earth and humanity.

In engineering ethics, the responsibility of engineers is often discussed in relation to code of ethics that articulate specific responsibilities of engineers. Such codes of ethics stress three types of responsibilities of engineers: (1) conducting the profession with integrity and honesty and in a competent way, (2) responsibilities towards employers and clients and (3) responsibility towards the public and society. With respect to the latter, most US codes of ethics maintain that engineers ‘should hold paramount the safety, health and welfare of the public’.

As has been pointed out by several authors (Nissenbaum 1996; Johnson & Powers 2005; Swierstra & Jelsma 2006), it may be hard to pinpoint individual responsibility in engineering. The reason is that the conditions for the proper attribution of individual responsibility that have been discussed in the philosophical literature (like freedom to act, knowledge, and causality) are often not met by individual engineers. For example, engineers may feel compelled to act in a certain way due to hierarchical or market constraints, and negative consequences may be very hard or impossible to predict beforehand. The causality condition is often difficult to meet as well due to the long chain from research and development of a technology till its use and the many people involved in this chain. Davis (2012) nevertheless maintains that despite such difficulties individual engineers can and do take responsibility.

One issue that is at stake in this debate is the notion of responsibility. Davis (2012), and also for example Ladd (1991), argue for a notion of responsibility that focuses less on blame and stresses the forward-looking or virtuous character of assuming responsibility. But many others focus on backward-looking notions of responsibility that stress accountability, blameworthiness or liability. Zandvoort (2000), for example has pleaded for a notion of responsibility in engineering that is more like the legal notion of strict liability, in which the knowledge condition for responsibility is seriously weakened. Doorn (2012) compares three perspectives on responsibility ascription in engineering—a merit-based, a right-based and a consequentialist perspective—and argues that the consequentialist perspective, which applies a forward-looking notion of responsibility, is most powerful in influencing engineering practice.

The difficulty of attributing individual responsibility may lead to the Problem of Many Hands (PMH). The term was first coined by Dennis Thompson (1980) in an article about the responsibility of public officials. The term is used to describe problems with the ascription of individual responsibility in collective settings. Doorn (2010) has proposed a procedurals approach, based on Rawls’ reflective equilibrium model, to deal with the PMH; other ways of dealing with the PMH include the design of institutions that help to avoid it or an emphasis on virtuous behavior in organizations (van de Poel, Royakers, & Zwart 2015).

Whereas the PMH refers to the problem of attributing responsibility among a collective of human agents, technological developments have also made it possible to allocate tasks to self-learning and intelligent systems. Such systems may function and learn in ways that are hard to understand, predict and control for humans, leading to so-called ‘responsibility gaps’ (Matthias 2004). Since knowledge and control are usually seen as (essential) preconditions for responsibility, lack thereof may make it increasingly difficult to hold humans responsible for the actions and consequences of intelligent systems.

Initially, such responsibility gaps were mainly discussed in relation to autonomous weapon systems and self-driving cars (Sparrow 2007; Danaher 2016). As a possible solution, the notion of meaningful human control has been proposed as a precondition for the development and employment of such systems to ensure that human can retain control, and hence responsibility over these systems (Santoni de Sio and van den Hoven 2018). Nyholm (2018) has argued that many alleged cases of responsibility gaps are better understood in terms collaborative human-technology agency (with humans in a supervising role) rather than the technology taking over control. While responsibility gaps may not impossible, the more difficult issue may be to attribute responsibility to the various humans involved (which brings the PMH back on the table).

More recently, responsibility gaps have become a more general concern in relation to AI. Due to the advance of machine learning, AI systems may learn in ways that are hard, or almost impossible to understand for humans. Initially, the dominant notion of responsibility addressed in the literature on responsibility gaps was blameworthiness or culpability, but Santoni de Sio and Mecacci (2021) have recently proposed to distinguish between what they call culpability gaps, moral accountability gaps, public accountability gaps, and active responsibility gaps.

In the last decades, increasingly attention is paid not only to ethical issues that arise during the use of a technology, but also during the design phase. An important consideration behind this development is the thought that during the design phase technologies, and their social consequences, are still malleable whereas during the use phase technologies are more or less given and negative social consequences may be harder to avoid or positive effects harder to achieve.

In computer ethics, an approach known as Value Sensitive Design (VSD) has been developed to explicitly address the ethical nature of design. VSD aims at integrating values of ethical importance in engineering design in a systematic way (Friedman & Hendry 2019). The approach combines conceptual, empirical and technical investigations. There is also a range of other approaches aimed at including values in design. ‘Design for X’ approaches in engineering aim at including instrumental values (like maintainability, reliability and costs) but they also include design for sustainability, inclusive design, and affective design (Holt & Barnes 2010). Inclusive design aims at making designs accessible to the whole population including, for example, handicapped people and the elderly (Erlandson 2008). Affective design aims at designs that evoke positive emotions with the users and so contributes to human well-being. Van de Hoven, Vermaas, and van de Poel 2015 gives a good overview of the state-of-the art of value sensitive design for various values and application domains.

If one tries to integrate values into design one may run into the problem of a conflict of values. The safest car is, due to its weight, not likely to be the most sustainability. Here safety and sustainability conflict in the design of cars. Traditional methods in which engineers deal with such conflicts and make trade-off between different requirements for design include cost-benefit analysis and multiple criteria analysis. Such methods are, however, beset with methodological problems like those discussed in Section 2.4 (Franssen 2005; Hansson 2007). Van de Poel (2009) discusses various alternatives for dealing with value conflicts in design including the setting of thresholds (satisficing), reasoning about values, innovation and diversity.

The risks of technology are one of the traditional ethical concerns in the ethics of technology. Risks raise not only ethical issues but other philosophical issues, such as epistemological and decision-theoretical issues as well (Roeser et al. 2012).

Risk is usually defined as the product of the probability of an undesirable event and the effect of that event, although there are also other definitions around (Hansson 2004b). In general it seems desirable to keep technological risks as small as possible. The larger the risk, the larger either the likeliness or the impact of an undesirable event is. Risk reduction therefore is an important goal in technological development and engineering codes of ethics often attribute a responsibility to engineers in reducing risks and designing safe products. Still, risk reduction is not always feasible or desirable. It is sometimes not feasible, because there are no absolutely safe products and technologies. But even if risk reduction is feasible it may not be acceptable from a moral point of view. Reducing risk often comes at a cost. Safer products may be more difficult to use, more expensive or less sustainable. So sooner or later, one is confronted with the question: what is safe enough? What makes a risk (un)acceptable?

The process of dealing with risks is often divided into three stages: risk assessment, risk evaluation and risk management. Of these, the second is most obviously ethically relevant. However, risk assessment already involves value judgments, for example about which risks should be assessed in the first place (Shrader-Frechette 1991). An important, and morally relevant, issue is also the degree of evidence that is needed to establish a risk. In establishing a risk on the basis of a body of empirical data one might make two kinds of mistakes. One can establish a risk when there is actually none (type I error) or one can mistakenly conclude that there is no risk while there actually is a risk (type II error). Science traditionally aims at avoiding type I errors. Several authors have argued that in the specific context of risk assessment it is often more important to avoid type II errors (Cranor 1990; Shrader-Frechette 1991). The reason for this is that risk assessment not just aims at establishing scientific truth but has a practical aim, i.e., to provide the knowledge on basis of which decisions can be made about whether it is desirable to reduce or avoid certain technological risks in order to protect users or the public.

Risk evaluation is carried out in a number of ways (see, e.g., Shrader-Frechette 1985). One possible approach is to judge the acceptability of risks by comparing them to other risks or to certain standards. One could, for example, compare technological risks with naturally occurring risks. This approach, however, runs the danger of committing a naturalistic fallacy: naturally occurring risks may (sometimes) be unavoidable but that does not necessarily make them morally acceptable. More generally, it is often dubious to judge the acceptability of the risk of technology A by comparing it to the risk of technology B if A and B are not alternatives in a decision (for this and other fallacies in reasoning about risks, see Hansson 2004a).

A second approach to risk evaluation is risk-cost benefit analysis, which is based on weighing the risks against the benefits of an activity. Different decision criteria can be applied if a (risk) cost benefit analysis is carried out (Kneese, Ben-David, and Schulze 1983). According to Hansson (2003: 306), usually the following criterion is applied:

… a risk is acceptable if and only if the total benefits that the exposure gives rise to outweigh the total risks, measured as the probability-weighted disutility of outcomes.

A third approach is to base risk acceptance on the consent of people who suffer the risks after they have been informed about these risks (informed consent). A problem of this approach is that technological risks usually affect a large number of people at once. Informed consent may therefore lead to a “society of stalemates” (Hansson 2003: 300).

Several authors have proposed alternatives to the traditional approaches of risk evaluation on the basis of philosophical and ethical arguments. Shrader-Frechette (1991) has proposed a number of reforms in risk assessment and evaluation procedures on the basis of a philosophical critique of current practices. Roeser (2012) argues for a role of emotions in judging the acceptability of risks. Hansson has proposed the following alternative principle for risk evaluation:

Exposure of a person to a risk is acceptable if and only if this exposure is part of an equitable social system of risk-taking that works to her advantage. (Hansson 2003: 305)

Hansson’s proposal introduces a number of moral considerations in risk evaluation that are traditionally not addressed or only marginally addressed. These are the consideration whether individuals profit from a risky activity and the consideration whether the distribution of risks and benefits is fair.

Questions about acceptable risk may also be framed in terms of risk imposition. The question is then under what conditions it is acceptable for some agent A to impose a risk on some other agent B. The criteria for acceptable risk imposition are in part similar to the ones discussed above. A risk imposition may, for example, be (more) acceptable if agent B gave their informed consent, or if the risky activity that generates the risk is beneficial for agent B. However, other considerations come in as well, like the relation between agent A and agent B. It might perhaps be acceptable for parents to impose certain risks on their children, while it would be improper for the government to impose such risks on children.

Risk impositions may particularly be problematic if they lead to domination or domination-like effects (Maheshwari and Nyholm 2022). Domination is here understood in the republican sense proposed by philosophers like Pettit (2012). Freedom from domination does not just require people to have different options to choose from, but also to be free from the (potential) arbitrary interference in the (availability of) these options by others. Non-domination thus requires that others do not have the power to arbitrary interfere with one’s options (whether that power is exercised or not). Risk imposition may lead to domination (or at least dominating-like effects) if agent A (the risk imposer) by imposing a risk on agent B (the risk bearer) can arbitrary affect the range of safe options available to agent B.

Some authors have criticized the focus on risks in the ethics of technology. One strand of criticism argues that we often lack the knowledge to reliably assess the risks of a new technology before it has come into use. We often do not know the probability that something might go wrong, and sometimes we even do not know, or at least not fully, what might go wrong and what possible negative consequences may be. To deal with this, some authors have proposed to conceive of the introduction of new technology in society as a social experiment and have urged to think about the conditions under which such experiments are morally acceptable (Martin & Schinzinger 2022; van de Poel 2016). Another strand of criticism states that the focus on risks has led to a reduction of the impacts of technology that are considered (Swierstra & te Molder 2012). Only impacts related to safety and health, which can be calculated as risks, are considered, whereas ‘soft’ impacts, for example of a social or psychological nature, are neglected, thereby impoverishing the moral evaluation of new technologies.

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Aristotle, Special Topics: causality | artifact | artificial intelligence: ethics of | Bacon, Francis | Chinese room argument | Church-Turing Thesis | computability and complexity | computer science, philosophy of | computing: and moral responsibility | episteme and techne [= scientific knowledge and expertise] | functionalism | identity: over time | identity: relative | information technology: and moral values | justice | justice: climate | knowledge how | material constitution | mind: computational theory of | moral responsibility | multiple realizability | Popper, Karl | practical reason | rationality: instrumental | responsibility: collective | risk | sortals | Turing machines | Turing test

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Graduate Writing Center

Introductions, thesis statements, and roadmaps - graduate writing center.

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Introductions, Thesis Statements, and Roadmaps

• Body Paragraphs and Topic Sentences
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The first paragraph or two of any paper should be constructed with care, creating a path for both the writer and reader to follow. However, it is very common to adjust the introduction more than once over the course of drafting and revising your document. In fact, it is normal (and often very useful, or even essential!) to heavily revise your introduction after you've finished composing the paper, since that is most likely when you have the best grasp on what you've been aiming to say.

The introduction is your opportunity to efficiently establish for your reader the topic and significance of your discussion, the focused argument or claim you’ll make contained in your thesis statement, and a sense of how your presentation of information will proceed.

There are a few things to avoid in crafting good introductions. Steer clear of unnecessary length: you should be able to effectively introduce the critical elements of any project a page or less. Another pitfall to watch out for is providing excessive history or context before clearly stating your own purpose. Finally, don’t lose time stalling because you can't think of a good first line. A funny or dramatic opener for your paper (also known as “a hook”) can be a nice touch, but it is by no means a required element in a good academic paper.

Introductions, Thesis Statements, and Roadmaps Links

• Short video (5:47): " Writing an Introduction to a Paper ," GWC
• Handout (printable):  " Introductions ," University of North Carolina Chapel Hill Writing Center
• Handout (printable): " Thesis Statements ," University of North Carolina Chapel Hill Writing Center
• NPS-specific one-page (printable)  S ample Thesis Chapter Introduction with Roadmap , from "Venezuela: A Revolution on Standby," Luis Calvo
• Short video (3:39):  " Writing Ninjas: How to Write a Strong Thesis Statement "
• Video (5:06): " Thesis Statements ," Purdue OWL

Writing Topics A–Z

This index links to the most relevant page for each item. Please email us at [email protected] if we're missing something!

A    B    C    D    E    F    G    H    I    J   K   L    M    N    O    P    Q   R    S    T    U    V    W   X  Y   Z

While Sandel argues that pursuing perfection through genetic engineering would decrease our sense of humility, he claims that the sense of solidarity we would lose is also important.

This thesis summarizes several points in Sandel’s argument, but it does not make a claim about how we should understand his argument. A reader who read Sandel’s argument would not also need to read an essay based on this descriptive thesis.  

Broad thesis (arguable, but difficult to support with evidence) 

Michael Sandel’s arguments about genetic engineering do not take into consideration all the relevant issues.

This is an arguable claim because it would be possible to argue against it by saying that Michael Sandel’s arguments do take all of the relevant issues into consideration. But the claim is too broad. Because the thesis does not specify which “issues” it is focused on—or why it matters if they are considered—readers won’t know what the rest of the essay will argue, and the writer won’t know what to focus on. If there is a particular issue that Sandel does not address, then a more specific version of the thesis would include that issue—hand an explanation of why it is important.  

Arguable thesis with analytical claim 

While Sandel argues persuasively that our instinct to “remake” (54) ourselves into something ever more perfect is a problem, his belief that we can always draw a line between what is medically necessary and what makes us simply “better than well” (51) is less convincing.

This is an arguable analytical claim. To argue for this claim, the essay writer will need to show how evidence from the article itself points to this interpretation. It’s also a reasonable scope for a thesis because it can be supported with evidence available in the text and is neither too broad nor too narrow.  

Arguable thesis with normative claim 

Given Sandel’s argument against genetic enhancement, we should not allow parents to decide on using Human Growth Hormone for their children.

This thesis tells us what we should do about a particular issue discussed in Sandel’s article, but it does not tell us how we should understand Sandel’s argument.  

Questions to ask about your thesis 

• Is the thesis truly arguable? Does it speak to a genuine dilemma in the source, or would most readers automatically agree with it?  
• Is the thesis too obvious? Again, would most or all readers agree with it without needing to see your argument?  
• Is the thesis complex enough to require a whole essay's worth of argument?  
• Is the thesis supportable with evidence from the text rather than with generalizations or outside research?  
• Would anyone want to read a paper in which this thesis was developed? That is, can you explain what this paper is adding to our understanding of a problem, question, or topic?
• picture_as_pdf Thesis

Thesis Statements

What this handout is about.

This handout describes what a thesis statement is, how thesis statements work in your writing, and how you can craft or refine one for your draft.

Introduction

Writing in college often takes the form of persuasion—convincing others that you have an interesting, logical point of view on the subject you are studying. Persuasion is a skill you practice regularly in your daily life. You persuade your roommate to clean up, your parents to let you borrow the car, your friend to vote for your favorite candidate or policy. In college, course assignments often ask you to make a persuasive case in writing. You are asked to convince your reader of your point of view. This form of persuasion, often called academic argument, follows a predictable pattern in writing. After a brief introduction of your topic, you state your point of view on the topic directly and often in one sentence. This sentence is the thesis statement, and it serves as a summary of the argument you’ll make in the rest of your paper.

What is a thesis statement?

A thesis statement:

• tells the reader how you will interpret the significance of the subject matter under discussion.
• is a road map for the paper; in other words, it tells the reader what to expect from the rest of the paper.
• directly answers the question asked of you. A thesis is an interpretation of a question or subject, not the subject itself. The subject, or topic, of an essay might be World War II or Moby Dick; a thesis must then offer a way to understand the war or the novel.
• makes a claim that others might dispute.
• is usually a single sentence near the beginning of your paper (most often, at the end of the first paragraph) that presents your argument to the reader. The rest of the paper, the body of the essay, gathers and organizes evidence that will persuade the reader of the logic of your interpretation.

If your assignment asks you to take a position or develop a claim about a subject, you may need to convey that position or claim in a thesis statement near the beginning of your draft. The assignment may not explicitly state that you need a thesis statement because your instructor may assume you will include one. When in doubt, ask your instructor if the assignment requires a thesis statement. When an assignment asks you to analyze, to interpret, to compare and contrast, to demonstrate cause and effect, or to take a stand on an issue, it is likely that you are being asked to develop a thesis and to support it persuasively. (Check out our handout on understanding assignments for more information.)

How do I create a thesis?

A thesis is the result of a lengthy thinking process. Formulating a thesis is not the first thing you do after reading an essay assignment. Before you develop an argument on any topic, you have to collect and organize evidence, look for possible relationships between known facts (such as surprising contrasts or similarities), and think about the significance of these relationships. Once you do this thinking, you will probably have a “working thesis” that presents a basic or main idea and an argument that you think you can support with evidence. Both the argument and your thesis are likely to need adjustment along the way.

Writers use all kinds of techniques to stimulate their thinking and to help them clarify relationships or comprehend the broader significance of a topic and arrive at a thesis statement. For more ideas on how to get started, see our handout on brainstorming .

How do I know if my thesis is strong?

If there’s time, run it by your instructor or make an appointment at the Writing Center to get some feedback. Even if you do not have time to get advice elsewhere, you can do some thesis evaluation of your own. When reviewing your first draft and its working thesis, ask yourself the following :

• Do I answer the question? Re-reading the question prompt after constructing a working thesis can help you fix an argument that misses the focus of the question. If the prompt isn’t phrased as a question, try to rephrase it. For example, “Discuss the effect of X on Y” can be rephrased as “What is the effect of X on Y?”
• Have I taken a position that others might challenge or oppose? If your thesis simply states facts that no one would, or even could, disagree with, it’s possible that you are simply providing a summary, rather than making an argument.
• Is my thesis statement specific enough? Thesis statements that are too vague often do not have a strong argument. If your thesis contains words like “good” or “successful,” see if you could be more specific: why is something “good”; what specifically makes something “successful”?
• Does my thesis pass the “So what?” test? If a reader’s first response is likely to  be “So what?” then you need to clarify, to forge a relationship, or to connect to a larger issue.
• Does my essay support my thesis specifically and without wandering? If your thesis and the body of your essay do not seem to go together, one of them has to change. It’s okay to change your working thesis to reflect things you have figured out in the course of writing your paper. Remember, always reassess and revise your writing as necessary.
• Does my thesis pass the “how and why?” test? If a reader’s first response is “how?” or “why?” your thesis may be too open-ended and lack guidance for the reader. See what you can add to give the reader a better take on your position right from the beginning.

Suppose you are taking a course on contemporary communication, and the instructor hands out the following essay assignment: “Discuss the impact of social media on public awareness.” Looking back at your notes, you might start with this working thesis:

Social media impacts public awareness in both positive and negative ways.

You can use the questions above to help you revise this general statement into a stronger thesis.

• Do I answer the question? You can analyze this if you rephrase “discuss the impact” as “what is the impact?” This way, you can see that you’ve answered the question only very generally with the vague “positive and negative ways.”
• Have I taken a position that others might challenge or oppose? Not likely. Only people who maintain that social media has a solely positive or solely negative impact could disagree.
• Is my thesis statement specific enough? No. What are the positive effects? What are the negative effects?
• Does my thesis pass the “how and why?” test? No. Why are they positive? How are they positive? What are their causes? Why are they negative? How are they negative? What are their causes?
• Does my thesis pass the “So what?” test? No. Why should anyone care about the positive and/or negative impact of social media?

After thinking about your answers to these questions, you decide to focus on the one impact you feel strongly about and have strong evidence for:

Because not every voice on social media is reliable, people have become much more critical consumers of information, and thus, more informed voters.

This version is a much stronger thesis! It answers the question, takes a specific position that others can challenge, and it gives a sense of why it matters.

Let’s try another. Suppose your literature professor hands out the following assignment in a class on the American novel: Write an analysis of some aspect of Mark Twain’s novel Huckleberry Finn. “This will be easy,” you think. “I loved Huckleberry Finn!” You grab a pad of paper and write:

Mark Twain’s Huckleberry Finn is a great American novel.

You begin to analyze your thesis:

• Do I answer the question? No. The prompt asks you to analyze some aspect of the novel. Your working thesis is a statement of general appreciation for the entire novel.

Think about aspects of the novel that are important to its structure or meaning—for example, the role of storytelling, the contrasting scenes between the shore and the river, or the relationships between adults and children. Now you write:

In Huckleberry Finn, Mark Twain develops a contrast between life on the river and life on the shore.

• Do I answer the question? Yes!
• Have I taken a position that others might challenge or oppose? Not really. This contrast is well-known and accepted.
• Is my thesis statement specific enough? It’s getting there–you have highlighted an important aspect of the novel for investigation. However, it’s still not clear what your analysis will reveal.
• Does my thesis pass the “how and why?” test? Not yet. Compare scenes from the book and see what you discover. Free write, make lists, jot down Huck’s actions and reactions and anything else that seems interesting.
• Does my thesis pass the “So what?” test? What’s the point of this contrast? What does it signify?”

After examining the evidence and considering your own insights, you write:

Through its contrasting river and shore scenes, Twain’s Huckleberry Finn suggests that to find the true expression of American democratic ideals, one must leave “civilized” society and go back to nature.

This final thesis statement presents an interpretation of a literary work based on an analysis of its content. Of course, for the essay itself to be successful, you must now present evidence from the novel that will convince the reader of your interpretation.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

Anson, Chris M., and Robert A. Schwegler. 2010. The Longman Handbook for Writers and Readers , 6th ed. New York: Longman.

Lunsford, Andrea A. 2015. The St. Martin’s Handbook , 8th ed. Boston: Bedford/St Martin’s.

Ramage, John D., John C. Bean, and June Johnson. 2018. The Allyn & Bacon Guide to Writing , 8th ed. New York: Pearson.

Ruszkiewicz, John J., Christy Friend, Daniel Seward, and Maxine Hairston. 2010. The Scott, Foresman Handbook for Writers , 9th ed. Boston: Pearson Education.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Writing a Paper: Thesis Statements

Basics of thesis statements.

The thesis statement is the brief articulation of your paper's central argument and purpose. You might hear it referred to as simply a "thesis." Every scholarly paper should have a thesis statement, and strong thesis statements are concise, specific, and arguable. Concise means the thesis is short: perhaps one or two sentences for a shorter paper. Specific means the thesis deals with a narrow and focused topic, appropriate to the paper's length. Arguable means that a scholar in your field could disagree (or perhaps already has!).

Strong thesis statements address specific intellectual questions, have clear positions, and use a structure that reflects the overall structure of the paper. Read on to learn more about constructing a strong thesis statement.

Being Specific

This thesis statement has no specific argument:

Needs Improvement: In this essay, I will examine two scholarly articles to find similarities and differences.

This statement is concise, but it is neither specific nor arguable—a reader might wonder, "Which scholarly articles? What is the topic of this paper? What field is the author writing in?" Additionally, the purpose of the paper—to "examine…to find similarities and differences" is not of a scholarly level. Identifying similarities and differences is a good first step, but strong academic argument goes further, analyzing what those similarities and differences might mean or imply.

Better: In this essay, I will argue that Bowler's (2003) autocratic management style, when coupled with Smith's (2007) theory of social cognition, can reduce the expenses associated with employee turnover.

The new revision here is still concise, as well as specific and arguable.  We can see that it is specific because the writer is mentioning (a) concrete ideas and (b) exact authors.  We can also gather the field (business) and the topic (management and employee turnover). The statement is arguable because the student goes beyond merely comparing; he or she draws conclusions from that comparison ("can reduce the expenses associated with employee turnover").

Making a Unique Argument

This thesis draft repeats the language of the writing prompt without making a unique argument:

Needs Improvement: The purpose of this essay is to monitor, assess, and evaluate an educational program for its strengths and weaknesses. Then, I will provide suggestions for improvement.

You can see here that the student has simply stated the paper's assignment, without articulating specifically how he or she will address it. The student can correct this error simply by phrasing the thesis statement as a specific answer to the assignment prompt.

Better: Through a series of student interviews, I found that Kennedy High School's antibullying program was ineffective. In order to address issues of conflict between students, I argue that Kennedy High School should embrace policies outlined by the California Department of Education (2010).

Words like "ineffective" and "argue" show here that the student has clearly thought through the assignment and analyzed the material; he or she is putting forth a specific and debatable position. The concrete information ("student interviews," "antibullying") further prepares the reader for the body of the paper and demonstrates how the student has addressed the assignment prompt without just restating that language.

Creating a Debate

This thesis statement includes only obvious fact or plot summary instead of argument:

Needs Improvement: Leadership is an important quality in nurse educators.

A good strategy to determine if your thesis statement is too broad (and therefore, not arguable) is to ask yourself, "Would a scholar in my field disagree with this point?" Here, we can see easily that no scholar is likely to argue that leadership is an unimportant quality in nurse educators.  The student needs to come up with a more arguable claim, and probably a narrower one; remember that a short paper needs a more focused topic than a dissertation.

Better: Roderick's (2009) theory of participatory leadership  is particularly appropriate to nurse educators working within the emergency medicine field, where students benefit most from collegial and kinesthetic learning.

Here, the student has identified a particular type of leadership ("participatory leadership"), narrowing the topic, and has made an arguable claim (this type of leadership is "appropriate" to a specific type of nurse educator). Conceivably, a scholar in the nursing field might disagree with this approach. The student's paper can now proceed, providing specific pieces of evidence to support the arguable central claim.

Choosing the Right Words

This thesis statement uses large or scholarly-sounding words that have no real substance:

Needs Improvement: Scholars should work to seize metacognitive outcomes by harnessing discipline-based networks to empower collaborative infrastructures.

There are many words in this sentence that may be buzzwords in the student's field or key terms taken from other texts, but together they do not communicate a clear, specific meaning. Sometimes students think scholarly writing means constructing complex sentences using special language, but actually it's usually a stronger choice to write clear, simple sentences. When in doubt, remember that your ideas should be complex, not your sentence structure.

Better: Ecologists should work to educate the U.S. public on conservation methods by making use of local and national green organizations to create a widespread communication plan.

Notice in the revision that the field is now clear (ecology), and the language has been made much more field-specific ("conservation methods," "green organizations"), so the reader is able to see concretely the ideas the student is communicating.

Leaving Room for Discussion

This thesis statement is not capable of development or advancement in the paper:

Needs Improvement: There are always alternatives to illegal drug use.

This sample thesis statement makes a claim, but it is not a claim that will sustain extended discussion. This claim is the type of claim that might be appropriate for the conclusion of a paper, but in the beginning of the paper, the student is left with nowhere to go. What further points can be made? If there are "always alternatives" to the problem the student is identifying, then why bother developing a paper around that claim? Ideally, a thesis statement should be complex enough to explore over the length of the entire paper.

Better: The most effective treatment plan for methamphetamine addiction may be a combination of pharmacological and cognitive therapy, as argued by Baker (2008), Smith (2009), and Xavier (2011).

In the revised thesis, you can see the student make a specific, debatable claim that has the potential to generate several pages' worth of discussion. When drafting a thesis statement, think about the questions your thesis statement will generate: What follow-up inquiries might a reader have? In the first example, there are almost no additional questions implied, but the revised example allows for a good deal more exploration.

Thesis Mad Libs

If you are having trouble getting started, try using the models below to generate a rough model of a thesis statement! These models are intended for drafting purposes only and should not appear in your final work.

• In this essay, I argue ____, using ______ to assert _____.
• While scholars have often argued ______, I argue______, because_______.
• Through an analysis of ______, I argue ______, which is important because_______.

Words to Avoid and to Embrace

When drafting your thesis statement, avoid words like explore, investigate, learn, compile, summarize , and explain to describe the main purpose of your paper. These words imply a paper that summarizes or "reports," rather than synthesizing and analyzing.

Instead of the terms above, try words like argue, critique, question , and interrogate . These more analytical words may help you begin strongly, by articulating a specific, critical, scholarly position.

Read Kayla's blog post for tips on taking a stand in a well-crafted thesis statement.

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What is a thesis statement? [with example]

What is a thesis statement?

Purpose of a thesis statement, how to write the best thesis statement, thesis statement example, frequently asked questions about thesis statements, related articles.

A thesis statement is a concise description of the goal of your work. This element is one of the most essential components of academic writing , as it tells your readers what they can expect in your paper.

A thesis statement is the main argument of your paper or thesis.

If you find yourself in the process of writing a a paper, but you don't know how to create a thesis statement , you've come to the right place. In the next paragraphs, you will learn about the most important elements of a thesis statement and how to come up with one.

A thesis statement highlights the main topic, shows how it will evolve, and conveys clearly the aim of your work. It does not only share the topic but it conveys the conclusion you came up with.

A good thesis statement provides directions for the development of the topic throughout the paper. In sum, this element of academic writing is crucial to sound research .

You can create a great thesis statement by following the format we outlined in our guide How to write a thesis statement .

In general, you should adhere to the following tips to write the best thesis statement:

• Focus the main idea of your thesis into one or two sentences.
• Write the answer to the main question of your topic.
• Clearly state your position in relation to the topic.
• Do not state the obvious. Give a disputable stance that requires evidence.

Tip: To check if your thesis is clear, explain it to a peer or colleague. If they are confused, then you likely need to re-write it.

Here's an example of a thesis statement:

In what follows, I will explore Fiorina’s position in more depth, focusing especially on her claim that technology is "a great tool for democratization." Ultimately, I argue that the primary problem with Fiorina’s stance lies in her laissez-faire understanding of technology. By granting technology a kind of independent existence apart from human motivation and intent—by positing that "technology permits anybody to play," as if it possesses an autonomy all its own—Fiorina unwittingly opens the way for the very kind of discriminatory and undemocratic mechanisms that her position seemingly rejects.

Tip: Once you finish your paper, return to your thesis statement to make sure that it reflects what you actually wrote.

A thesis statement is part of the introduction of your paper. It is usually found in the first or second paragraph to let the reader know your research purpose from the beginning.

In general, a thesis statement should have one or two sentences. It really depends on your academic and expertise level. Take a look at our guide about the length of thesis statements, for more insight on this topic.

Here is a list of Thesis Statement Examples that will help you understand better how to write them.

Yes. Every good essay should include a thesis statement as part of its introduction. Of course, if you are a high school student you are not expected to have an extremely elaborate statement. A couple of clear sentences indicating the aim of your essay will be more than enough.

Here is a great YouTube tutorial showing How To Write An Essay: Thesis Statements .

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Home » Thesis – Structure, Example and Writing Guide

Thesis – Structure, Example and Writing Guide

Table of contents.

Definition:

Thesis is a scholarly document that presents a student’s original research and findings on a particular topic or question. It is usually written as a requirement for a graduate degree program and is intended to demonstrate the student’s mastery of the subject matter and their ability to conduct independent research.

History of Thesis

The concept of a thesis can be traced back to ancient Greece, where it was used as a way for students to demonstrate their knowledge of a particular subject. However, the modern form of the thesis as a scholarly document used to earn a degree is a relatively recent development.

The origin of the modern thesis can be traced back to medieval universities in Europe. During this time, students were required to present a “disputation” in which they would defend a particular thesis in front of their peers and faculty members. These disputations served as a way to demonstrate the student’s mastery of the subject matter and were often the final requirement for earning a degree.

In the 17th century, the concept of the thesis was formalized further with the creation of the modern research university. Students were now required to complete a research project and present their findings in a written document, which would serve as the basis for their degree.

The modern thesis as we know it today has evolved over time, with different disciplines and institutions adopting their own standards and formats. However, the basic elements of a thesis – original research, a clear research question, a thorough review of the literature, and a well-argued conclusion – remain the same.

Structure of Thesis

The structure of a thesis may vary slightly depending on the specific requirements of the institution, department, or field of study, but generally, it follows a specific format.

Here’s a breakdown of the structure of a thesis:

This is the first page of the thesis that includes the title of the thesis, the name of the author, the name of the institution, the department, the date, and any other relevant information required by the institution.

This is a brief summary of the thesis that provides an overview of the research question, methodology, findings, and conclusions.

This page provides a list of all the chapters and sections in the thesis and their page numbers.

Introduction

This chapter provides an overview of the research question, the context of the research, and the purpose of the study. The introduction should also outline the methodology and the scope of the research.

Literature Review

This chapter provides a critical analysis of the relevant literature on the research topic. It should demonstrate the gap in the existing knowledge and justify the need for the research.

Methodology

This chapter provides a detailed description of the research methods used to gather and analyze data. It should explain the research design, the sampling method, data collection techniques, and data analysis procedures.

This chapter presents the findings of the research. It should include tables, graphs, and charts to illustrate the results.

This chapter interprets the results and relates them to the research question. It should explain the significance of the findings and their implications for the research topic.

This chapter summarizes the key findings and the main conclusions of the research. It should also provide recommendations for future research.

This section provides a list of all the sources cited in the thesis. The citation style may vary depending on the requirements of the institution or the field of study.

This section includes any additional material that supports the research, such as raw data, survey questionnaires, or other relevant documents.

How to write Thesis

Here are some steps to help you write a thesis:

• Choose a Topic: The first step in writing a thesis is to choose a topic that interests you and is relevant to your field of study. You should also consider the scope of the topic and the availability of resources for research.
• Develop a Research Question: Once you have chosen a topic, you need to develop a research question that you will answer in your thesis. The research question should be specific, clear, and feasible.
• Conduct a Literature Review: Before you start your research, you need to conduct a literature review to identify the existing knowledge and gaps in the field. This will help you refine your research question and develop a research methodology.
• Develop a Research Methodology: Once you have refined your research question, you need to develop a research methodology that includes the research design, data collection methods, and data analysis procedures.
• Collect and Analyze Data: After developing your research methodology, you need to collect and analyze data. This may involve conducting surveys, interviews, experiments, or analyzing existing data.
• Write the Thesis: Once you have analyzed the data, you need to write the thesis. The thesis should follow a specific structure that includes an introduction, literature review, methodology, results, discussion, conclusion, and references.
• Edit and Proofread: After completing the thesis, you need to edit and proofread it carefully. You should also have someone else review it to ensure that it is clear, concise, and free of errors.
• Submit the Thesis: Finally, you need to submit the thesis to your academic advisor or committee for review and evaluation.

Example of Thesis

Example of Thesis template for Students:

Title of Thesis

Table of Contents:

Chapter 1: Introduction

Chapter 2: Literature Review

Chapter 3: Research Methodology

Chapter 4: Results

Chapter 5: Discussion

Chapter 6: Conclusion

References:

Appendices:

Note: That’s just a basic template, but it should give you an idea of the structure and content that a typical thesis might include. Be sure to consult with your department or supervisor for any specific formatting requirements they may have. Good luck with your thesis!

Application of Thesis

Thesis is an important academic document that serves several purposes. Here are some of the applications of thesis:

• Academic Requirement: A thesis is a requirement for many academic programs, especially at the graduate level. It is an essential component of the evaluation process and demonstrates the student’s ability to conduct original research and contribute to the knowledge in their field.
• Career Advancement: A thesis can also help in career advancement. Employers often value candidates who have completed a thesis as it demonstrates their research skills, critical thinking abilities, and their dedication to their field of study.
• Publication : A thesis can serve as a basis for future publications in academic journals, books, or conference proceedings. It provides the researcher with an opportunity to present their research to a wider audience and contribute to the body of knowledge in their field.
• Personal Development: Writing a thesis is a challenging task that requires time, dedication, and perseverance. It provides the student with an opportunity to develop critical thinking, research, and writing skills that are essential for their personal and professional development.
• Impact on Society: The findings of a thesis can have an impact on society by addressing important issues, providing insights into complex problems, and contributing to the development of policies and practices.

Purpose of Thesis

The purpose of a thesis is to present original research findings in a clear and organized manner. It is a formal document that demonstrates a student’s ability to conduct independent research and contribute to the knowledge in their field of study. The primary purposes of a thesis are:

• To Contribute to Knowledge: The main purpose of a thesis is to contribute to the knowledge in a particular field of study. By conducting original research and presenting their findings, the student adds new insights and perspectives to the existing body of knowledge.
• To Demonstrate Research Skills: A thesis is an opportunity for the student to demonstrate their research skills. This includes the ability to formulate a research question, design a research methodology, collect and analyze data, and draw conclusions based on their findings.
• To Develop Critical Thinking: Writing a thesis requires critical thinking and analysis. The student must evaluate existing literature and identify gaps in the field, as well as develop and defend their own ideas.
• To Provide Evidence of Competence : A thesis provides evidence of the student’s competence in their field of study. It demonstrates their ability to apply theoretical concepts to real-world problems, and their ability to communicate their ideas effectively.
• To Facilitate Career Advancement : Completing a thesis can help the student advance their career by demonstrating their research skills and dedication to their field of study. It can also provide a basis for future publications, presentations, or research projects.

When to Write Thesis

The timing for writing a thesis depends on the specific requirements of the academic program or institution. In most cases, the opportunity to write a thesis is typically offered at the graduate level, but there may be exceptions.

Generally, students should plan to write their thesis during the final year of their graduate program. This allows sufficient time for conducting research, analyzing data, and writing the thesis. It is important to start planning the thesis early and to identify a research topic and research advisor as soon as possible.

In some cases, students may be able to write a thesis as part of an undergraduate program or as an independent research project outside of an academic program. In such cases, it is important to consult with faculty advisors or mentors to ensure that the research is appropriately designed and executed.

It is important to note that the process of writing a thesis can be time-consuming and requires a significant amount of effort and dedication. It is important to plan accordingly and to allocate sufficient time for conducting research, analyzing data, and writing the thesis.

Characteristics of Thesis

The characteristics of a thesis vary depending on the specific academic program or institution. However, some general characteristics of a thesis include:

• Originality : A thesis should present original research findings or insights. It should demonstrate the student’s ability to conduct independent research and contribute to the knowledge in their field of study.
• Clarity : A thesis should be clear and concise. It should present the research question, methodology, findings, and conclusions in a logical and organized manner. It should also be well-written, with proper grammar, spelling, and punctuation.
• Research-Based: A thesis should be based on rigorous research, which involves collecting and analyzing data from various sources. The research should be well-designed, with appropriate research methods and techniques.
• Evidence-Based : A thesis should be based on evidence, which means that all claims made in the thesis should be supported by data or literature. The evidence should be properly cited using appropriate citation styles.
• Critical Thinking: A thesis should demonstrate the student’s ability to critically analyze and evaluate information. It should present the student’s own ideas and arguments, and engage with existing literature in the field.
• Academic Style : A thesis should adhere to the conventions of academic writing. It should be well-structured, with clear headings and subheadings, and should use appropriate academic language.

Advantages of Thesis

There are several advantages to writing a thesis, including:

• Development of Research Skills: Writing a thesis requires extensive research and analytical skills. It helps to develop the student’s research skills, including the ability to formulate research questions, design and execute research methodologies, collect and analyze data, and draw conclusions based on their findings.
• Contribution to Knowledge: Writing a thesis provides an opportunity for the student to contribute to the knowledge in their field of study. By conducting original research, they can add new insights and perspectives to the existing body of knowledge.
• Preparation for Future Research: Completing a thesis prepares the student for future research projects. It provides them with the necessary skills to design and execute research methodologies, analyze data, and draw conclusions based on their findings.
• Career Advancement: Writing a thesis can help to advance the student’s career. It demonstrates their research skills and dedication to their field of study, and provides a basis for future publications, presentations, or research projects.
• Personal Growth: Completing a thesis can be a challenging and rewarding experience. It requires dedication, hard work, and perseverance. It can help the student to develop self-confidence, independence, and a sense of accomplishment.

Limitations of Thesis

There are also some limitations to writing a thesis, including:

• Time and Resources: Writing a thesis requires a significant amount of time and resources. It can be a time-consuming and expensive process, as it may involve conducting original research, analyzing data, and producing a lengthy document.
• Narrow Focus: A thesis is typically focused on a specific research question or topic, which may limit the student’s exposure to other areas within their field of study.
• Limited Audience: A thesis is usually only read by a small number of people, such as the student’s thesis advisor and committee members. This limits the potential impact of the research findings.
• Lack of Real-World Application : Some thesis topics may be highly theoretical or academic in nature, which may limit their practical application in the real world.
• Pressure and Stress : Writing a thesis can be a stressful and pressure-filled experience, as it may involve meeting strict deadlines, conducting original research, and producing a high-quality document.
• Potential for Isolation: Writing a thesis can be a solitary experience, as the student may spend a significant amount of time working independently on their research and writing.

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• Dissertation

How to Write a Thesis or Dissertation Introduction

Published on September 7, 2022 by Tegan George and Shona McCombes. Revised on November 21, 2023.

The introduction is the first section of your thesis or dissertation , appearing right after the table of contents . Your introduction draws your reader in, setting the stage for your research with a clear focus, purpose, and direction on a relevant topic .

Your introduction should include:

• Your topic, in context: what does your reader need to know to understand your thesis dissertation?
• Your focus and scope: what specific aspect of the topic will you address?
• The relevance of your research: how does your work fit into existing studies on your topic?
• Your questions and objectives: what does your research aim to find out, and how?
• An overview of your structure: what does each section contribute to the overall aim?

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Table of contents

How to start your introduction, topic and context, focus and scope, relevance and importance, questions and objectives, overview of the structure, thesis introduction example, introduction checklist, other interesting articles, frequently asked questions about introductions.

Although your introduction kicks off your dissertation, it doesn’t have to be the first thing you write — in fact, it’s often one of the very last parts to be completed (just before your abstract ).

It’s a good idea to write a rough draft of your introduction as you begin your research, to help guide you. If you wrote a research proposal , consider using this as a template, as it contains many of the same elements. However, be sure to revise your introduction throughout the writing process, making sure it matches the content of your ensuing sections.

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Begin by introducing your dissertation topic and giving any necessary background information. It’s important to contextualize your research and generate interest. Aim to show why your topic is timely or important. You may want to mention a relevant news item, academic debate, or practical problem.

After a brief introduction to your general area of interest, narrow your focus and define the scope of your research.

You can narrow this down in many ways, such as by:

• Geographical area
• Time period
• Demographics or communities
• Themes or aspects of the topic

It’s essential to share your motivation for doing this research, as well as how it relates to existing work on your topic. Further, you should also mention what new insights you expect it will contribute.

Start by giving a brief overview of the current state of research. You should definitely cite the most relevant literature, but remember that you will conduct a more in-depth survey of relevant sources in the literature review section, so there’s no need to go too in-depth in the introduction.

Depending on your field, the importance of your research might focus on its practical application (e.g., in policy or management) or on advancing scholarly understanding of the topic (e.g., by developing theories or adding new empirical data). In many cases, it will do both.

Ultimately, your introduction should explain how your thesis or dissertation:

• Helps solve a practical or theoretical problem
• Addresses a gap in the literature
• Builds on existing research
• Proposes a new understanding of your topic

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Perhaps the most important part of your introduction is your questions and objectives, as it sets up the expectations for the rest of your thesis or dissertation. How you formulate your research questions and research objectives will depend on your discipline, topic, and focus, but you should always clearly state the central aim of your research.

If your research aims to test hypotheses , you can formulate them here. Your introduction is also a good place for a conceptual framework that suggests relationships between variables .

• Conduct surveys to collect data on students’ levels of knowledge, understanding, and positive/negative perceptions of government policy.
• Determine whether attitudes to climate policy are associated with variables such as age, gender, region, and social class.
• Conduct interviews to gain qualitative insights into students’ perspectives and actions in relation to climate policy.

To help guide your reader, end your introduction with an outline  of the structure of the thesis or dissertation to follow. Share a brief summary of each chapter, clearly showing how each contributes to your central aims. However, be careful to keep this overview concise: 1-2 sentences should be enough.

I. Introduction

Human language consists of a set of vowels and consonants which are combined to form words. During the speech production process, thoughts are converted into spoken utterances to convey a message. The appropriate words and their meanings are selected in the mental lexicon (Dell & Burger, 1997). This pre-verbal message is then grammatically coded, during which a syntactic representation of the utterance is built.

Speech, language, and voice disorders affect the vocal cords, nerves, muscles, and brain structures, which result in a distorted language reception or speech production (Sataloff & Hawkshaw, 2014). The symptoms vary from adding superfluous words and taking pauses to hoarseness of the voice, depending on the type of disorder (Dodd, 2005). However, distortions of the speech may also occur as a result of a disease that seems unrelated to speech, such as multiple sclerosis or chronic obstructive pulmonary disease.

This study aims to determine which acoustic parameters are suitable for the automatic detection of exacerbations in patients suffering from chronic obstructive pulmonary disease (COPD) by investigating which aspects of speech differ between COPD patients and healthy speakers and which aspects differ between COPD patients in exacerbation and stable COPD patients.

Checklist: Introduction

I have introduced my research topic in an engaging way.

I have provided necessary context to help the reader understand my topic.

I have clearly specified the focus of my research.

I have shown the relevance and importance of the dissertation topic .

I have clearly stated the problem or question that my research addresses.

I have outlined the specific objectives of the research .

I have provided an overview of the dissertation’s structure .

You've written a strong introduction for your thesis or dissertation. Use the other checklists to continue improving your dissertation.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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The introduction of a research paper includes several key elements:

• A hook to catch the reader’s interest
• Relevant background on the topic
• Details of your research problem

and your problem statement

• A thesis statement or research question
• Sometimes an overview of the paper

Don’t feel that you have to write the introduction first. The introduction is often one of the last parts of the research paper you’ll write, along with the conclusion.

This is because it can be easier to introduce your paper once you’ve already written the body ; you may not have the clearest idea of your arguments until you’ve written them, and things can change during the writing process .

Research objectives describe what you intend your research project to accomplish.

They summarize the approach and purpose of the project and help to focus your research.

Your objectives should appear in the introduction of your research paper , at the end of your problem statement .

Scope of research is determined at the beginning of your research process , prior to the data collection stage. Sometimes called “scope of study,” your scope delineates what will and will not be covered in your project. It helps you focus your work and your time, ensuring that you’ll be able to achieve your goals and outcomes.

Defining a scope can be very useful in any research project, from a research proposal to a thesis or dissertation . A scope is needed for all types of research: quantitative , qualitative , and mixed methods .

To define your scope of research, consider the following:

• Budget constraints or any specifics of grant funding
• Your proposed timeline and duration
• Specifics about your population of study, your proposed sample size , and the research methodology you’ll pursue
• Any inclusion and exclusion criteria
• Any anticipated control , extraneous , or confounding variables that could bias your research if not accounted for properly.

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• Literary Terms
• Definition & Examples
• When & How to Write a Thesis

I. What is a Thesis?

The thesis (pronounced thee -seez), also known as a thesis statement, is the sentence that introduces the main argument or point of view of a composition (formal essay, nonfiction piece, or narrative). It is the main claim that the author is making about that topic and serves to summarize and introduce that writing that will be discussed throughout the entire piece. For this reason, the thesis is typically found within the first introduction paragraph.

II. Examples of Theses

Here are a few examples of theses which may be found in the introductions of a variety of essays :

In “The Mending Wall,” Robert Frost uses imagery, metaphor, and dialogue to argue against the use of fences between neighbors.

In this example, the thesis introduces the main subject (Frost’s poem “The Mending Wall”), aspects of the subject which will be examined (imagery, metaphor, and dialogue) and the writer’s argument (fences should not be used).

While Facebook connects some, overall, the social networking site is negative in that it isolates users, causes jealousy, and becomes an addiction.

This thesis introduces an argumentative essay which argues against the use of Facebook due to three of its negative effects.

During the college application process, I discovered my willingness to work hard to achieve my dreams and just what those dreams were.

In this more personal example, the thesis statement introduces a narrative essay which will focus on personal development in realizing one’s goals and how to achieve them.

III. The Importance of Using a Thesis

Theses are absolutely necessary components in essays because they introduce what an essay will be about. Without a thesis, the essay lacks clear organization and direction. Theses allow writers to organize their ideas by clearly stating them, and they allow readers to be aware from the beginning of a composition’s subject, argument, and course. Thesis statements must precisely express an argument within the introductory paragraph of the piece in order to guide the reader from the very beginning.

IV. Examples of Theses in Literature

For examples of theses in literature, consider these thesis statements from essays about topics in literature:

In William Shakespeare’s “ Sonnet 46,” both physicality and emotion together form powerful romantic love.

This thesis statement clearly states the work and its author as well as the main argument: physicality and emotion create romantic love.

In The Scarlet Letter, Nathaniel Hawthorne symbolically shows Hester Prynne’s developing identity through the use of the letter A: she moves from adulteress to able community member to angel.

In this example, the work and author are introduced as well as the main argument and supporting points: Prynne’s identity is shown through the letter A in three ways: adulteress, able community member, and angel.

John Keats’ poem “To Autumn” utilizes rhythm, rhyme, and imagery to examine autumn’s simultaneous birth and decay.

This thesis statement introduces the poem and its author along with an argument about the nature of autumn. This argument will be supported by an examination of rhythm, rhyme, and imagery.

V. Examples of Theses in Pop Culture

Sometimes, pop culture attempts to make arguments similar to those of research papers and essays. Here are a few examples of theses in pop culture:

America’s food industry is making a killing and it’s making us sick, but you have the power to turn the tables.

The documentary Food Inc. examines this thesis with evidence throughout the film including video evidence, interviews with experts, and scientific research.

Orca whales should not be kept in captivity, as it is psychologically traumatizing and has caused them to kill their own trainers.

Blackfish uses footage, interviews, and history to argue for the thesis that orca whales should not be held in captivity.

VI. Related Terms

Just as a thesis is introduced in the beginning of a composition, the hypothesis is considered a starting point as well. Whereas a thesis introduces the main point of an essay, the hypothesis introduces a proposed explanation which is being investigated through scientific or mathematical research. Thesis statements present arguments based on evidence which is presented throughout the paper, whereas hypotheses are being tested by scientists and mathematicians who may disprove or prove them through experimentation. Here is an example of a hypothesis versus a thesis:

Hypothesis:

Students skip school more often as summer vacation approaches.

This hypothesis could be tested by examining attendance records and interviewing students. It may or may not be true.

Students skip school due to sickness, boredom with classes, and the urge to rebel.

This thesis presents an argument which will be examined and supported in the paper with detailed evidence and research.

Introduction

A paper’s introduction is its first paragraph which is used to introduce the paper’s main aim and points used to support that aim throughout the paper. The thesis statement is the most important part of the introduction which states all of this information in one concise statement. Typically, introduction paragraphs require a thesis statement which ties together the entire introduction and introduces the rest of the paper.

VII. Conclusion

Theses are necessary components of well-organized and convincing essays, nonfiction pieces, narratives , and documentaries. They allow writers to organize and support arguments to be developed throughout a composition, and they allow readers to understand from the beginning what the aim of the composition is.

List of Terms

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Where should I put the "Definition of concepts" section in a Research Paper

I'm writing a research paper and there are some concepts which I think would help a reader to understand the study better. However, I'm not sure where to put this section. Should I put it right after the Introduction? Or before Literature Review?

• writing-style

• I decided to put it below the introduction since in this way it helps reader to understand the experiment better. –  Majid Hassanpour Commented Jul 9, 2015 at 7:10

4 Answers 4

I would put the section in question before the first section, where the concepts you want to define are mentioned. However, note that, generally, you have two options , in my opinion. The first is to collect definitions (potentially, with brief explanations) under a separate section , which is usually called "Definitions of Terms". The second option is not to have a separate section, but to present the concepts' definitions and explanations as your paper's story line unfolds. While the benefit of having a separate section is clarity and ease of use for less advanced readers, the advantage of embedding concepts' definitions and explanations into the paper's main text is an opportunity to provide much more detailed explanations as well as smooth integration with the rest of material.

• @MajidHassanpour: You're welcome. –  Aleksandr Blekh Commented Jul 9, 2015 at 19:05
• I agree with this answer, If you do choose to include a "glossary" or "Definitions of Terms", it should go at the beginning of your paper. –  Ihab Shoully Commented Jan 31, 2023 at 13:50

Conventions like this vary between fields. Look at other papers in your field or subfield, and do what they do.

Definitions of key concepts are important to the understanding of your paper. Hence, it is preferable to have them as a separate section under the title "Definition of terms." This section should be be placed towards the beginning of the paper, before you start with the major content. I would place it in the introduction, immediately after the statement of the problem at hand and the purpose of the study.

Right in the introduction / Background. That's where you introduce everything, including concepts the reader needs to know.

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How to Write the Definition of Terms in Chapter 1 of a Thesis

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The Craft of Writing a Strong Hypothesis

Table of Contents

Writing a hypothesis is one of the essential elements of a scientific research paper. It needs to be to the point, clearly communicating what your research is trying to accomplish. A blurry, drawn-out, or complexly-structured hypothesis can confuse your readers. Or worse, the editor and peer reviewers.

A captivating hypothesis is not too intricate. This blog will take you through the process so that, by the end of it, you have a better idea of how to convey your research paper's intent in just one sentence.

What is a Hypothesis?

The first step in your scientific endeavor, a hypothesis, is a strong, concise statement that forms the basis of your research. It is not the same as a thesis statement , which is a brief summary of your research paper .

The sole purpose of a hypothesis is to predict your paper's findings, data, and conclusion. It comes from a place of curiosity and intuition . When you write a hypothesis, you're essentially making an educated guess based on scientific prejudices and evidence, which is further proven or disproven through the scientific method.

The reason for undertaking research is to observe a specific phenomenon. A hypothesis, therefore, lays out what the said phenomenon is. And it does so through two variables, an independent and dependent variable.

The independent variable is the cause behind the observation, while the dependent variable is the effect of the cause. A good example of this is “mixing red and blue forms purple.” In this hypothesis, mixing red and blue is the independent variable as you're combining the two colors at your own will. The formation of purple is the dependent variable as, in this case, it is conditional to the independent variable.

Different Types of Hypotheses‌

Types of hypotheses

Some would stand by the notion that there are only two types of hypotheses: a Null hypothesis and an Alternative hypothesis. While that may have some truth to it, it would be better to fully distinguish the most common forms as these terms come up so often, which might leave you out of context.

Apart from Null and Alternative, there are Complex, Simple, Directional, Non-Directional, Statistical, and Associative and casual hypotheses. They don't necessarily have to be exclusive, as one hypothesis can tick many boxes, but knowing the distinctions between them will make it easier for you to construct your own.

1. Null hypothesis

A null hypothesis proposes no relationship between two variables. Denoted by H 0 , it is a negative statement like “Attending physiotherapy sessions does not affect athletes' on-field performance.” Here, the author claims physiotherapy sessions have no effect on on-field performances. Even if there is, it's only a coincidence.

2. Alternative hypothesis

Considered to be the opposite of a null hypothesis, an alternative hypothesis is donated as H1 or Ha. It explicitly states that the dependent variable affects the independent variable. A good  alternative hypothesis example is “Attending physiotherapy sessions improves athletes' on-field performance.” or “Water evaporates at 100 °C. ” The alternative hypothesis further branches into directional and non-directional.

• Directional hypothesis: A hypothesis that states the result would be either positive or negative is called directional hypothesis. It accompanies H1 with either the ‘<' or ‘>' sign.
• Non-directional hypothesis: A non-directional hypothesis only claims an effect on the dependent variable. It does not clarify whether the result would be positive or negative. The sign for a non-directional hypothesis is ‘≠.'

3. Simple hypothesis

A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, “Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking.

4. Complex hypothesis

In contrast to a simple hypothesis, a complex hypothesis implies the relationship between multiple independent and dependent variables. For instance, “Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism.” The independent variable is eating more fruits, while the dependent variables are higher immunity, lesser cholesterol, and high metabolism.

5. Associative and casual hypothesis

Associative and casual hypotheses don't exhibit how many variables there will be. They define the relationship between the variables. In an associative hypothesis, changing any one variable, dependent or independent, affects others. In a casual hypothesis, the independent variable directly affects the dependent.

6. Empirical hypothesis

Also referred to as the working hypothesis, an empirical hypothesis claims a theory's validation via experiments and observation. This way, the statement appears justifiable and different from a wild guess.

Say, the hypothesis is “Women who take iron tablets face a lesser risk of anemia than those who take vitamin B12.” This is an example of an empirical hypothesis where the researcher  the statement after assessing a group of women who take iron tablets and charting the findings.

7. Statistical hypothesis

The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like “44% of the Indian population belong in the age group of 22-27.” leverage evidence to prove or disprove a particular statement.

Characteristics of a Good Hypothesis

Writing a hypothesis is essential as it can make or break your research for you. That includes your chances of getting published in a journal. So when you're designing one, keep an eye out for these pointers:

• A research hypothesis has to be simple yet clear to look justifiable enough.
• It has to be testable — your research would be rendered pointless if too far-fetched into reality or limited by technology.
• It has to be precise about the results —what you are trying to do and achieve through it should come out in your hypothesis.
• A research hypothesis should be self-explanatory, leaving no doubt in the reader's mind.
• If you are developing a relational hypothesis, you need to include the variables and establish an appropriate relationship among them.
• A hypothesis must keep and reflect the scope for further investigations and experiments.

Separating a Hypothesis from a Prediction

Outside of academia, hypothesis and prediction are often used interchangeably. In research writing, this is not only confusing but also incorrect. And although a hypothesis and prediction are guesses at their core, there are many differences between them.

A hypothesis is an educated guess or even a testable prediction validated through research. It aims to analyze the gathered evidence and facts to define a relationship between variables and put forth a logical explanation behind the nature of events.

Predictions are assumptions or expected outcomes made without any backing evidence. They are more fictionally inclined regardless of where they originate from.

For this reason, a hypothesis holds much more weight than a prediction. It sticks to the scientific method rather than pure guesswork. "Planets revolve around the Sun." is an example of a hypothesis as it is previous knowledge and observed trends. Additionally, we can test it through the scientific method.

Whereas "COVID-19 will be eradicated by 2030." is a prediction. Even though it results from past trends, we can't prove or disprove it. So, the only way this gets validated is to wait and watch if COVID-19 cases end by 2030.

Finally, How to Write a Hypothesis

Quick tips on writing a hypothesis

1.  Be clear about your research question

A hypothesis should instantly address the research question or the problem statement. To do so, you need to ask a question. Understand the constraints of your undertaken research topic and then formulate a simple and topic-centric problem. Only after that can you develop a hypothesis and further test for evidence.

2. Carry out a recce

Once you have your research's foundation laid out, it would be best to conduct preliminary research. Go through previous theories, academic papers, data, and experiments before you start curating your research hypothesis. It will give you an idea of your hypothesis's viability or originality.

Making use of references from relevant research papers helps draft a good research hypothesis. SciSpace Discover offers a repository of over 270 million research papers to browse through and gain a deeper understanding of related studies on a particular topic. Additionally, you can use SciSpace Copilot , your AI research assistant, for reading any lengthy research paper and getting a more summarized context of it. A hypothesis can be formed after evaluating many such summarized research papers. Copilot also offers explanations for theories and equations, explains paper in simplified version, allows you to highlight any text in the paper or clip math equations and tables and provides a deeper, clear understanding of what is being said. This can improve the hypothesis by helping you identify potential research gaps.

3. Create a 3-dimensional hypothesis

Variables are an essential part of any reasonable hypothesis. So, identify your independent and dependent variable(s) and form a correlation between them. The ideal way to do this is to write the hypothetical assumption in the ‘if-then' form. If you use this form, make sure that you state the predefined relationship between the variables.

In another way, you can choose to present your hypothesis as a comparison between two variables. Here, you must specify the difference you expect to observe in the results.

4. Write the first draft

Now that everything is in place, it's time to write your hypothesis. For starters, create the first draft. In this version, write what you expect to find from your research.

Clearly separate your independent and dependent variables and the link between them. Don't fixate on syntax at this stage. The goal is to ensure your hypothesis addresses the issue.

5. Proof your hypothesis

After preparing the first draft of your hypothesis, you need to inspect it thoroughly. It should tick all the boxes, like being concise, straightforward, relevant, and accurate. Your final hypothesis has to be well-structured as well.

Research projects are an exciting and crucial part of being a scholar. And once you have your research question, you need a great hypothesis to begin conducting research. Thus, knowing how to write a hypothesis is very important.

Now that you have a firmer grasp on what a good hypothesis constitutes, the different kinds there are, and what process to follow, you will find it much easier to write your hypothesis, which ultimately helps your research.

Now it's easier than ever to streamline your research workflow with SciSpace Discover . Its integrated, comprehensive end-to-end platform for research allows scholars to easily discover, write and publish their research and fosters collaboration.

It includes everything you need, including a repository of over 270 million research papers across disciplines, SEO-optimized summaries and public profiles to show your expertise and experience.

If you found these tips on writing a research hypothesis useful, head over to our blog on Statistical Hypothesis Testing to learn about the top researchers, papers, and institutions in this domain.

Frequently Asked Questions (FAQs)

1. what is the definition of hypothesis.

According to the Oxford dictionary, a hypothesis is defined as “An idea or explanation of something that is based on a few known facts, but that has not yet been proved to be true or correct”.

2. What is an example of hypothesis?

The hypothesis is a statement that proposes a relationship between two or more variables. An example: "If we increase the number of new users who join our platform by 25%, then we will see an increase in revenue."

3. What is an example of null hypothesis?

A null hypothesis is a statement that there is no relationship between two variables. The null hypothesis is written as H0. The null hypothesis states that there is no effect. For example, if you're studying whether or not a particular type of exercise increases strength, your null hypothesis will be "there is no difference in strength between people who exercise and people who don't."

4. What are the types of research?

• Fundamental research

• Applied research

• Qualitative research

• Quantitative research

• Mixed research

• Exploratory research

• Longitudinal research

• Cross-sectional research

• Field research

• Laboratory research

• Fixed research

• Flexible research

• Action research

• Policy research

• Classification research

• Comparative research

• Causal research

• Inductive research

• Deductive research

5. How to write a hypothesis?

• Your hypothesis should be able to predict the relationship and outcome.

• Avoid wordiness by keeping it simple and brief.

• Your hypothesis should contain observable and testable outcomes.

• Your hypothesis should be relevant to the research question.

6. What are the 2 types of hypothesis?

• Null hypotheses are used to test the claim that "there is no difference between two groups of data".

• Alternative hypotheses test the claim that "there is a difference between two data groups".

7. Difference between research question and research hypothesis?

A research question is a broad, open-ended question you will try to answer through your research. A hypothesis is a statement based on prior research or theory that you expect to be true due to your study. Example - Research question: What are the factors that influence the adoption of the new technology? Research hypothesis: There is a positive relationship between age, education and income level with the adoption of the new technology.

8. What is plural for hypothesis?

The plural of hypothesis is hypotheses. Here's an example of how it would be used in a statement, "Numerous well-considered hypotheses are presented in this part, and they are supported by tables and figures that are well-illustrated."

9. What is the red queen hypothesis?

The red queen hypothesis in evolutionary biology states that species must constantly evolve to avoid extinction because if they don't, they will be outcompeted by other species that are evolving. Leigh Van Valen first proposed it in 1973; since then, it has been tested and substantiated many times.

10. Who is known as the father of null hypothesis?

The father of the null hypothesis is Sir Ronald Fisher. He published a paper in 1925 that introduced the concept of null hypothesis testing, and he was also the first to use the term itself.

11. When to reject null hypothesis?

You need to find a significant difference between your two populations to reject the null hypothesis. You can determine that by running statistical tests such as an independent sample t-test or a dependent sample t-test. You should reject the null hypothesis if the p-value is less than 0.05.

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Definition of thesis

Did you know.

In high school, college, or graduate school, students often have to write a thesis on a topic in their major field of study. In many fields, a final thesis is the biggest challenge involved in getting a master's degree, and the same is true for students studying for a Ph.D. (a Ph.D. thesis is often called a dissertation ). But a thesis may also be an idea; so in the course of the paper the student may put forth several theses (notice the plural form) and attempt to prove them.

Examples of thesis in a Sentence

These examples are programmatically compiled from various online sources to illustrate current usage of the word 'thesis.' Any opinions expressed in the examples do not represent those of Merriam-Webster or its editors. Send us feedback about these examples.

Word History

in sense 3, Middle English, lowering of the voice, from Late Latin & Greek; Late Latin, from Greek, downbeat, more important part of a foot, literally, act of laying down; in other senses, Latin, from Greek, literally, act of laying down, from tithenai to put, lay down — more at do

14th century, in the meaning defined at sense 3a(1)

Dictionary Entries Near thesis

the sins of the fathers are visited upon the children

thesis novel

Cite this Entry

“Thesis.” Merriam-Webster.com Dictionary , Merriam-Webster, https://www.merriam-webster.com/dictionary/thesis. Accessed 13 Jul. 2024.

Kids Definition

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Britannica.com: Encyclopedia article about thesis

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• What Is a Thesis? | Ultimate Guide & Examples

What Is a Thesis? | Ultimate Guide & Examples

Published on 15 September 2022 by Tegan George . Revised on 5 December 2023.

A thesis is a type of research paper based on your original research. It is usually submitted as the final step of a PhD program in the UK.

Writing a thesis can be a daunting experience. Indeed, alongside a dissertation , it is the longest piece of writing students typically complete. It relies on your ability to conduct research from start to finish: designing your research , collecting data , developing a robust analysis, drawing strong conclusions , and writing concisely .

Thesis template

You can also download our full thesis template in the format of your choice below. Our template includes a ready-made table of contents , as well as guidance for what each chapter should include. It’s easy to make it your own, and can help you get started.

Download Word template Download Google Docs template

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Table of contents

Thesis vs. thesis statement, how to structure a thesis, acknowledgements or preface, list of figures and tables, list of abbreviations, introduction, literature review, methodology, reference list, proofreading and editing, defending your thesis, frequently asked questions about theses.

You may have heard the word thesis as a standalone term or as a component of academic writing called a thesis statement . Keep in mind that these are two very different things.

• A thesis statement is a very common component of an essay, particularly in the humanities. It usually comprises 1 or 2 sentences in the introduction of your essay , and should clearly and concisely summarise the central points of your academic essay .
• A thesis is a long-form piece of academic writing, often taking more than a full semester to complete. It is generally a degree requirement to complete a PhD program.
• In many countries, particularly the UK, a dissertation is generally written at the bachelor’s or master’s level.
• In the US, a dissertation is generally written as a final step toward obtaining a PhD.

Prevent plagiarism, run a free check.

The final structure of your thesis depends on a variety of components, such as:

• Your discipline
• Your theoretical approach

Humanities theses are often structured more like a longer-form essay . Just like in an essay, you build an argument to support a central thesis.

In both hard and social sciences, theses typically include an introduction , literature review , methodology section ,  results section , discussion section , and conclusion section . These are each presented in their own dedicated section or chapter. In some cases, you might want to add an appendix .

Thesis examples

We’ve compiled a short list of thesis examples to help you get started.

• Example thesis #1:   ‘Abolition, Africans, and Abstraction: the Influence of the “Noble Savage” on British and French Antislavery Thought, 1787-1807’ by Suchait Kahlon.
• Example thesis #2: ‘”A Starving Man Helping Another Starving Man”: UNRRA, India, and the Genesis of Global Relief, 1943-1947’ by Julian Saint Reiman.

The very first page of your thesis contains all necessary identifying information, including:

• Your full title
• Your full name
• Your department
• Your institution and degree program
• Your submission date.

Sometimes the title page also includes your student ID, the name of your supervisor, or the university’s logo. Check out your university’s guidelines if you’re not sure.

Read more about title pages

The acknowledgements section is usually optional. Its main point is to allow you to thank everyone who helped you in your thesis journey, such as supervisors, friends, or family. You can also choose to write a preface , but it’s typically one or the other, not both.

Read more about acknowledgements Read more about prefaces

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An abstract is a short summary of your thesis. Usually a maximum of 300 words long, it’s should include brief descriptions of your research objectives , methods, results, and conclusions. Though it may seem short, it introduces your work to your audience, serving as a first impression of your thesis.

Read more about abstracts

A table of contents lists all of your sections, plus their corresponding page numbers and subheadings if you have them. This helps your reader seamlessly navigate your document.

Your table of contents should include all the major parts of your thesis. In particular, don’t forget the the appendices. If you used heading styles, it’s easy to generate an automatic table Microsoft Word.

Read more about tables of contents

While not mandatory, if you used a lot of tables and/or figures, it’s nice to include a list of them to help guide your reader. It’s also easy to generate one of these in Word: just use the ‘Insert Caption’ feature.

Read more about lists of figures and tables

If you have used a lot of industry- or field-specific abbreviations in your thesis, you should include them in an alphabetised list of abbreviations . This way, your readers can easily look up any meanings they aren’t familiar with.

Read more about lists of abbreviations

Relatedly, if you find yourself using a lot of very specialised or field-specific terms that may not be familiar to your reader, consider including a glossary . Alphabetise the terms you want to include with a brief definition.

Read more about glossaries

An introduction sets up the topic, purpose, and relevance of your thesis, as well as expectations for your reader. This should:

• Ground your research topic , sharing any background information your reader may need
• Define the scope of your work
• Introduce any existing research on your topic, situating your work within a broader problem or debate
• State your research question(s)
• Outline (briefly) how the remainder of your work will proceed

In other words, your introduction should clearly and concisely show your reader the “what, why, and how” of your research.

Read more about introductions

A literature review helps you gain a robust understanding of any extant academic work on your topic, encompassing:

• Selecting relevant sources
• Determining the credibility of your sources
• Critically evaluating each of your sources
• Drawing connections between sources, including any themes, patterns, conflicts, or gaps

A literature review is not merely a summary of existing work. Rather, your literature review should ultimately lead to a clear justification for your own research, perhaps via:

• Addressing a gap in the literature
• Building on existing knowledge to draw new conclusions
• Exploring a new theoretical or methodological approach
• Introducing a new solution to an unresolved problem
• Definitively advocating for one side of a theoretical debate

Read more about literature reviews

Theoretical framework

Your literature review can often form the basis for your theoretical framework, but these are not the same thing. A theoretical framework defines and analyses the concepts and theories that your research hinges on.

Read more about theoretical frameworks

Your methodology chapter shows your reader how you conducted your research. It should be written clearly and methodically, easily allowing your reader to critically assess the credibility of your argument. Furthermore, your methods section should convince your reader that your method was the best way to answer your research question.

A methodology section should generally include:

• Your overall approach ( quantitative vs. qualitative )
• Your research methods (e.g., a longitudinal study )
• Your data collection methods (e.g., interviews or a controlled experiment
• Any tools or materials you used (e.g., computer software)
• The data analysis methods you chose (e.g., statistical analysis , discourse analysis )
• A strong, but not defensive justification of your methods

Read more about methodology sections

Your results section should highlight what your methodology discovered. These two sections work in tandem, but shouldn’t repeat each other. While your results section can include hypotheses or themes, don’t include any speculation or new arguments here.

Your results section should:

• State each (relevant) result with any (relevant) descriptive statistics (e.g., mean , standard deviation ) and inferential statistics (e.g., test statistics , p values )
• Explain how each result relates to the research question
• Determine whether the hypothesis was supported

Additional data (like raw numbers or interview transcripts ) can be included as an appendix . You can include tables and figures, but only if they help the reader better understand your results.

Read more about results sections

Your discussion section is where you can interpret your results in detail. Did they meet your expectations? How well do they fit within the framework that you built? You can refer back to any relevant source material to situate your results within your field, but leave most of that analysis in your literature review.

For any unexpected results, offer explanations or alternative interpretations of your data.

Read more about discussion sections

Your thesis conclusion should concisely answer your main research question. It should leave your reader with an ultra-clear understanding of your central argument, and emphasise what your research specifically has contributed to your field.

Why does your research matter? What recommendations for future research do you have? Lastly, wrap up your work with any concluding remarks.

Read more about conclusions

In order to avoid plagiarism , don’t forget to include a full reference list at the end of your thesis, citing the sources that you used. Choose one citation style and follow it consistently throughout your thesis, taking note of the formatting requirements of each style.

Which style you choose is often set by your department or your field, but common styles include MLA , Chicago , and APA.

Create APA citations Create MLA citations

In order to stay clear and concise, your thesis should include the most essential information needed to answer your research question. However, chances are you have many contributing documents, like interview transcripts or survey questions . These can be added as appendices , to save space in the main body.

Read more about appendices

Once you’re done writing, the next part of your editing process begins. Leave plenty of time for proofreading and editing prior to submission. Nothing looks worse than grammar mistakes or sloppy spelling errors!

Consider using a professional thesis editing service to make sure your final project is perfect.

Once you’ve submitted your final product, it’s common practice to have a thesis defense, an oral component of your finished work. This is scheduled by your advisor or committee, and usually entails a presentation and Q&A session.

After your defense, your committee will meet to determine if you deserve any departmental honors or accolades. However, keep in mind that defenses are usually just a formality. If there are any serious issues with your work, these should be resolved with your advisor way before a defense.

The conclusion of your thesis or dissertation shouldn’t take up more than 5-7% of your overall word count.

When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

If you only used a few abbreviations in your thesis or dissertation, you don’t necessarily need to include a list of abbreviations .

If your abbreviations are numerous, or if you think they won’t be known to your audience, it’s never a bad idea to add one. They can also improve readability, minimising confusion about abbreviations unfamiliar to your reader.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organise your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

Generally, an outline contains information on the different sections included in your thesis or dissertation, such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the ‘Cite this Scribbr article’ button to automatically add the citation to our free Reference Generator.

George, T. (2023, December 05). What Is a Thesis? | Ultimate Guide & Examples. Scribbr. Retrieved 10 July 2024, from https://www.scribbr.co.uk/thesis-dissertation/thesis-ultimate-guide/

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Europe is slapping tariffs on Chinese electric vehicles — for now. Here’s what to know

FILE - Jerry Gan, CEO of Geely Auto Group unveils the Galaxy Starship a new technology flagship AI-driven SUV prototype during Auto China 2024 in Beijing, Thursday, April 25, 2024. The European Union threatened on Wednesday, june 12, 2024, to hike tariffs on Chinese electric vehicles, escalating a trade dispute over Beijing’s subsidies for the exports that Brussels worries is hurting domestic automakers. (AP Photo/Ng Han Guan, File)

FILE - Visitors look at cars at the BYD booth during the China Auto Show in Beijing, China, Friday, April 26, 2024. The European Union threatened on Wednesday, june 12, 2024, to hike tariffs on Chinese electric vehicles, escalating a trade dispute over Beijing’s subsidies for the exports that Brussels worries is hurting domestic automakers. (AP Photo/Tatan Syuflana, File)

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FRANKFURT, Germany (AP) — The European Union is imposing sharply higher customs duties on electric vehicles imported from China. EVs are the latest flash point in a broader trade dispute over Chinese government subsidies and Beijing’s burgeoning exports of green technology to the 27-nation bloc.

The higher duties go into effect on Friday, pending a final decision in four month’s time.

Here are some basic facts about the EU’s planned customs duties:

What did the European Union do?

After an eight-month investigation, the European Commission, the EU’s executive arm, found that companies making electric cars in China benefit from massive government help that means they can undercut rivals in the EU on prices, take a big market share and threaten European jobs.

It announced the higher duties on June 12 and they go into effect from Friday. The duties are provisional, meaning they will be totaled up but won’t need to be paid until they’re confirmed by a vote of EU governments before Nov. 2. The EU will only collect the duties if there’s a further finding that the European auto industry would have suffered material harm without them.

That gives the EU and the Chinese government time to negotiate. Talks have been held between Valdis Dombrovskis, the EU commissioner for the economy, and Chinese Trade Minister Wang Wentao, as well as at the level of technical experts.

The higher duties are not a goal in themselves but “a means to correct an imbalance,” commission spokesman Eric Mamer said Thursday. “We certainly hope we can come to a solution which would allow us not to have to move forward on this path.”

The rates, if applied, would be: 17.4% on cars from BYD, 19.9% on those from Geely and 37.6% for vehicles exported by China’s state-owned SAIC. Geely has brands including Polestar and Sweden’s Volvo , while SAIC owns Britain’s MG, one of Europe’s bestselling EV brands. Other EV manufacturers in China including Western companies such as Volkswagen, BMW and Tesla would be subject to duties of at least 20.8%. The commission mentioned that Tesla might get an “individually calculated” rate if duties are definitively imposed.

Under EU rules it’s possible — though at present it seems unlikely — that the higher duties could be blocked ahead of the Nov. 2 effective date by vote of what the EU calls a “qualified majority” of countries. That means at least 15 of the 27 EU member governments representing at least 65% of the bloc’s population.

Why did the commission take action?

Chinese-built electric cars jumped from 3.9% of the EV market in 2020 to 25% by September 2023, the commission said, in part by unfairly undercutting EU industry prices.

The commission says companies in China accomplished that with the help of subsidies all along the chain of production, from cheap land for factories from local governments to below-market supplies of lithium and batteries from state-owned enterprises to tax breaks and below-interest financing from state-controlled banks.

The rapid growth in market share has sparked fears that Chinese cars will eventually threaten the EU’s ability to produce its own green technology needed to combat climate change, as well as the jobs of 2.5 million workers at risk in the auto industry and 10.3 million more people whose jobs depend indirectly on EV production.

Subsidized solar panels from China have wiped out European producers — an experience that European governments don’t want to see repeated with their auto industry.

Unusually, the commission acted on its own, without a complaint from the European auto industry. Industry leaders and Germany, home to BMW, Volkswagen and Mercedes-Benz, have been skeptics about the subsidy investigation. That’s because many of the cars that will be hit with tariffs are made by European companies, and because China could retaliate against the auto industry or in other areas.

How do the EU tariffs compare to ones announced by the U.S.?

The Biden administration is raising tariffs on Chinese EVs to 100% from the current 25%. At that level, the U.S. tariffs block virtually all Chinese EV imports.

That’s not what Europe is trying to do.

EU officials want affordable electric cars from abroad to achieve their goals of cutting greenhouse gas emissions by 55% by 2030 — but without the subsidies EU leaders see as unfair competition

The planned tariffs are aimed at leveling the playing field by approximating the size of the excess or unfair subsidies available to Chinese carmakers.

European countries subsidize electric cars, too. The question in trade disputes is whether subsidies are fair and available to all carmakers or distort the market in favor of one side.

Just how cheap are Chinese EVs?

Chinese carmakers have learned to make electric vehicles cheaply amid ferocious price competition at home in the world’s largest car market.

BYD’s Seal U Comfort model sells for the equivalent of 21,769 euros ($23,370) in China but 41,990 euros ($45,078) in Europe, according to Rhodium Group figures. The base model of BYD’s compact Seagull , due to arrive in Europe next year, sells for the equivalent of around $10,000 in China.

What does this mean for European drivers and carmakers?

It’s not clear what impact the duties will have on car prices. Chinese carmakers are able to make some cars so cheaply that they could absorb the duties in the form of lower profits instead of raising their prices.

While consumers might benefit from cheaper Chinese cars in the short term, allowing unfair practices could eventually mean less competition and higher prices in the long term, the commission argues.

Currently, Chinese carmakers often sell their vehicles in Europe at much higher prices than the same cars fetch in China, meaning they are favoring profits over market share, even given their recent market gains. Five of BYD’s six models would still earn a profit in Europe even at a 30% tariff, according to Rhodium Group calculations.

The fear is Europe is that Chinese competitors will turn to lowering their prices closer to the ones they are charging in China. and gain an even bigger chunk of the market.

How is China likely to react?

Beijing was sharply critical of the higher duties when they were announced, calling them “a naked act of protectionism.”

On Thursday, He Yadong, a spokesperson for the Chinese Commerce Ministry, said that the two sides had held several rounds of technical consultations and noted that a final EU ruling won’t be made for four months.

“It is hoped that the European side and the Chinese side will move in the same direction, show sincerity, expedite the consultation process and reach a mutually acceptable solution as soon as possible on the basis of facts and rules,” he said at a weekly media briefing in Beijing.

He also said that China hopes the EU will seriously listen to the voices of the European automakers and governments that have come out against the tariffs and avoid anti-subsidy measures that would harm cooperation between the Chinese and European auto industries.

It’s not clear what agreement might look like. One move could be to agree on minimum prices for Chinese cars.

China could retaliate against European products such as pork or brandy imports, or against European luxury car imports.

Over the longer term, Chinese carmakers could avoid tariffs by making cars in Europe. BYD is building a plant in Hungary, while Chery has a joint venture to build cars in Spain’s Catalonia region.

Moritsugu reported from Beijing.

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• Science and Technology Directorate

Feature Article: The Question of Who You Are

The Science and Technology Directorate (S&T) and the U.S. Citizenship and Immigration Service (USCIS) have joined forces to issue digital credentials using openly developed, free to implement internet standards. Here’s what this means and why it matters.

One of the critical challenges of our technology-driven, interconnected world is identity.

Without even speaking a word, we identify ourselves every day and in many different ways. Perhaps you enter a PIN to sign-in to a bank account or use a password to login to your health benefits. You scan your own face to unlock your phone to access some of the apps running on it. You swipe an ID card with a magnetic stripe to enter your office building. And of course, when you travel or work abroad, you must identify yourself with a passport. But what are you sharing when you identify yourself? Where does that identifying number or document come from, and who controls access to it?

S&T is working to help make your identity more secure, and to put control over your privacy and personal information into your own hands. Jared Goodwin, Chief of the Document Management Division within the Office of Intake and Document Production (OIDP) at USCIS, was also working on these issues. OIDP is tasked with the production of all immigration documents—they design the documents and acquire the vendors to produce them. USCIS wants to be able to issue digital credentials, like a green card, to a smartphone, which would be easier to carry and use, more secure, and it could be supported online. Actions like renewing and modifying immigration status would not require standing in line at an office somewhere.

Jared discovered S&T’s Silicon Valley Innovation Program was exploring similar solutions. “They’re going out to industry to look for ways to partner with agencies to prevent forgery and the counterfeiting of certificates and licenses,” he said. Jared contacted SVIP and the solution that they settled on together is to use two openly developed, global standards called Verifiable Credentials Data Model (VCDM) and Decentralized Identifiers (DID).

Created by the World Wide Web Consortium (W3C), a global standards development organization, with the support of S&T, USCIS, and many other like-minded partners, these standards describe how a secure, privacy respecting digital credentialing process can be implemented.

DIDs are unique identifiers that can be assigned to organizations, devices, or people. A DID, unlike a social security number, functions solely as an identifier and cannot be used for verification, as that role is deliberately separated and implemented using public key cryptography.

VCDM is a way to express credentials in a way that is cryptographically secure, privacy respecting, and machine verifiable. In addition, this standard enables a person to minimize the disclosure of personal data by implementing selective disclosure capabilities.

Selective disclosure allows digital credentials to contain many pieces of information but gives the user discretion to share only the specific information required for a particular transaction with the government or non-government entities, rather than disclosing the entire contents of the credential. So, the ability to selectively share, with consent, only pieces of information needed for a particular encounter is a highly desired capability.

Consider this example: a customer attempts to purchase a six-pack of beer at a convenience store. The way it works now, the cashier asks for an ID to verify the customer is old enough to buy liquor, but when they hand over their driver’s license…what else are they handing over?

Think about that very common transaction for a moment: a state-issued document from a department of motor vehicles, which is intended to demonstrate the qualification to drive a car, is presented to verify that you are older than 21. This document shares your date of birth, address, ID number, organ donation status, if you need to wear glasses, even your height and weight.

Part of the promise of the W3C standards is the ability to share only the data required for a transaction. In the scenario above, when the cashier asks for proof that you are older than 21, the customer could use the digital Permanent Resident Card on their phone to prove their verified age without sharing any other information (not even a specific date of birth). This is an important step towards putting privacy back in the hands of the people.

The DHS Privacy Office , charged with “embedding and enforcing privacy protections and transparency in all DHS activities,” has been brought into the process to review the W3C VCDM/DID framework and advise on any potential issues. 

“Beyond ensuring global interoperability, standards developed by the W3C undergo wide reviews that ensure that they incorporate security, privacy, accessibility, and internationalization,” said SVIP Managing Director Melissa Oh, “by helping implement these standards in our digital credentialing efforts, S&T, through SVIP, is helping to ensure that the technologies we use make a difference for people in how they secure their digital transactions and protect their privacy.”

“Going forward, the government wants to ensure individuals have agency and control over their digital interactions,” said Goodwin. “The user should be able to own their identity and decide when to share it, and we don’t want a system that has to reach back to an agency for verification.”

Thanks to the work of SVIP, USCIS and many others, digital credentials using W3C VCDM and W3C DID standards are going to become more and more common in the near future. The work will make a big difference preventing identity theft and forgery, allowing individuals to control their own personal information and privacy, especially online.

For related media inquiries, please contact [email protected] .

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Creating Stability Is Just as Important as Managing Change

• Ashley Goodall

To do their best work, employees need to feel a sense of consistency — not constant upheaval.

When we think about change at work today, we tend to assume its inevitability and focus our attention on how to manage it — what methods and processes and technology and communication we need to put in place to have it move ahead more smoothly. Of course, some change is necessary, and some is inevitable. But not all of it. What the scientific literature on predictability, agency, belonging, place, and meaning suggests is that before we think about managing change, we should consider the conditions that people need at work in order to be productive. In this article, the author explains why we should cultivate a renewed appreciation for the virtues of stability, together with an understanding of how to practice “stability management.”

Imagine, for a moment, being on the receiving end of the sort of communication that typically heralds a change at work. An email, say, announces a reorganization to be carried out over the course of the next few months. The language is cheery and optimistic, and talks in upbeat terms about the many opportunities that will flow from the latest transformation or realignment.

• Ashley Goodall is a leadership expert who has spent his career exploring large organizations from the inside, most recently as an executive at Cisco. He is the coauthor of Nine Lies About Work , which was selected as the best management book of 2019 by Strategy + Business and as one of Amazon’s best business and leadership books of 2019. Prior to Cisco, he spent fourteen years at Deloitte as a consultant and as the Chief Learning Officer for Leadership and Professional Development. His latest book, The Problem with Change , is available now.

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What are AI agents? 

The next big thing is AI tools that can do more complex tasks. Here’s how they will work.

• Melissa Heikkilä archive page

MIT Technology Review Explains: Let our writers untangle the complex, messy world of technology to help you understand what's coming next. You can read more from the series here.

When ChatGPT was first released, everyone in AI was talking about the new generation of AI assistants. But over the past year, that excitement has turned to a new target: AI agents. 

Agents featured prominently in Google’s annual I/O conference in May, when the company unveiled its new AI agent called Astra , which allows users to interact with it using audio and video. OpenAI’s new GPT-4o model has also been called an AI agent.  

And it’s not just hype, although there is definitely some of that too. Tech companies are plowing vast sums into creating AI agents, and their research efforts could usher in the kind of useful AI we have been dreaming about for decades. Many experts, including Sam Altman , say they are the next big thing.   

But what are they? And how can we use them? 

How are they defined? 

It is still early days for research into AI agents, and the field does not have a definitive definition for them. But simply, they are AI models and algorithms that can autonomously make decisions in a dynamic world, says Jim Fan, a senior research scientist at Nvidia who leads the company’s AI agents initiative. 

The grand vision for AI agents is a system that can execute a vast range of tasks, much like a human assistant. In the future, it could help you book your vacation, but it will also remember if you prefer swanky hotels, so it will only suggest hotels that have four stars or more and then go ahead and book the one you pick from the range of options it offers you. It will then also suggest flights that work best with your calendar, and plan the itinerary for your trip according to your preferences. It could make a list of things to pack based on that plan and the weather forecast. It might even send your itinerary to any friends it knows live in your destination and invite them along. In the workplace, it  could analyze your to-do list and execute tasks from it, such as sending calendar invites, memos, or emails. 

One vision for agents is that they are multimodal, meaning they can process language, audio, and video. For example, in Google’s Astra demo, users could point a smartphone camera at things and ask the agent questions. The agent could respond to text, audio, and video inputs. 

These agents could also make processes smoother for businesses and public organizations, says David Barber, the director of the University College London Centre for Artificial Intelligence. For example, an AI agent might be able to function as a more sophisticated customer service bot. The current generation of language-model-based assistants can only generate the next likely word in a sentence. But an AI agent would have the ability to act on natural-language commands autonomously and process customer service tasks without supervision. For example, the agent would be able to analyze customer complaint emails and then know to check the customer’s reference number, access databases such as customer relationship management and delivery systems to see whether the complaint is legitimate, and process it according to the company’s policies, Barber says. 

Broadly speaking, there are two different categories of agents, says Fan: software agents and embodied agents. 

Software agents run on computers or mobile phones and use apps, much as in the travel agent example above. “Those agents are very useful for office work or sending emails or having this chain of events going on,” he says. 

Embodied agents are agents that are situated in a 3D world such as a video game, or in a robot. These kinds of agents might make video games more engaging by letting people play with nonplayer characters controlled by AI. These sorts of agents could also help build more useful robots that could help us with everyday tasks at home, such as folding laundry and cooking meals. 

Fan was part of a team that built an embodied AI agent called MineDojo in the popular computer game Minecraft. Using a vast trove of data collected from the internet, Fan’s AI agent was able to learn new skills and tasks that allowed it to freely explore the virtual 3D world and complete complex tasks such as encircling llamas with fences or scooping lava into a bucket. Video games are good proxies for the real world, because they require agents to understand physics, reasoning, and common sense. 

In a new paper , which has not yet been peer-reviewed, researchers at Princeton say that AI agents tend to have three different characteristics. AI systems are considered “agentic” if they can pursue difficult goals without being instructed in complex environments. They also qualify if they can be instructed in natural language and act autonomously without supervision. And finally, the term “agent” can also apply to systems that are able to use tools, such as web search or programming, or are capable of planning. 

Are they a new thing?

The term “AI agents” has been around for years and has meant different things at different times, says Chirag Shah, a computer science professor at the University of Washington. 

There have been two waves of agents, says Fan. The current wave is thanks to the language model boom and the rise of systems such as ChatGPT. 

The previous wave was in 2016, when Google DeepMind introduced AlphaGo, its AI system that can play—and win—the game Go. AlphaGo was able to make decisions and plan strategies. This relied on reinforcement learning, a technique that rewards AI algorithms for desirable behaviors. 

“But these agents were not general,” says Oriol Vinyals, vice president of research at Google DeepMind. They were created for very specific tasks—in this case, playing Go. The new generation of foundation-model-based AI makes agents more universal, as they can learn from the world humans interact with. 

“You feel much more that the model is interacting with the world and then giving back to you better answers or better assisted assistance or whatnot,” says Vinyals. 

What are the limitations? 

There are still many open questions that need to be answered. Kanjun Qiu, CEO and founder of the AI startup Imbue, which is working on agents that can reason and code, likens the state of agents to where self-driving cars were just over a decade ago. They can do stuff, but they’re unreliable and still not really autonomous. For example, a coding agent can generate code, but it sometimes gets it wrong, and it doesn’t know how to test the code it’s creating, says Qiu. So humans still need to be actively involved in the process. AI systems still can’t fully reason, which is a critical step in operating in a complex and  ambiguous human world. 

“We’re nowhere close to having an agent that can just automate all of these chores for us,” says Fan. Current systems “hallucinate and they also don’t always follow instructions closely,” Fan says. “And that becomes annoying.”  

Another limitation is that after a while, AI agents lose track of what they are working on. AI systems are limited by their context windows, meaning the amount of data they can take into account at any given time. 

“ChatGPT can do coding, but it’s not able to do long-form content well. But for human developers, we look at an entire GitHub repository that has tens if not hundreds of lines of code, and we have no trouble navigating it,” says Fan. 

To tackle this problem, Google has increased its models’ capacity to process data , which allows users to have longer interactions with them in which they remember more about past interactions. The company said it is working on making its context windows infinite in the future.

For embodied agents such as robots, there are even more limitations. There is not enough training data to teach them, and researchers are only just starting to harness the power of foundation models in robotics. 

So amid all the hype and excitement, it’s worth bearing in mind that research into AI agents is still in its very early stages, and it will likely take years until we can experience their full potential. 

That sounds cool. Can I try an AI agent now? 

Sort of. You’ve most likely tried their early prototypes, such as OpenAI’s ChatGPT and GPT-4. “If you’re interacting with software that feels smart, that is kind of an agent,” says Qiu. 

Right now the best agents we have are systems with very narrow and specific use cases, such as coding assistants, customer service bots, or workflow automation software like Zapier, she says. But these are a far cry from a universal AI agent that can do complex tasks. 

“Today we have these computers and they’re really powerful, but we have to micromanage them,” says Qiu. 

OpenAI’s ChatGPT plug-ins, which allow people to create AI-powered assistants for web browsers, were an attempt at agents, says Qiu. But these systems are still clumsy, unreliable, and not capable of reasoning, she says. 

Despite that, these systems will one day change the way we interact with technology, Qiu believes, and it is a trend people need to pay attention to. 

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