Society, Ethics & Economy

Fairness, work, information, wellbeing and the economy. 89 entries live here, and 306 from other domains touch it.

Essential

AI bubbleThe worry that investors are pouring far more money into AI companies, chips and data centers than AI will earn back soon, so that prices could crash as in the dot-com bust of 2000.
AI ethicsThe study of what is right and wrong in how AI is built and used: who it may harm, who is responsible when it fails, and which values it should respect.
algorithmic biasUnfairness in software that judges or serves people: it consistently produces worse results for some groups, such as women, older people or ethnic minorities, often because it learned from skewed or incomplete records.
automationUsing machines or software to do work that people would otherwise do, from factory robots to programs that sort email or write reports.
deepfakeA fake but realistic video, photo or voice recording made with AI that puts words in a real person's mouth or shows them in a scene that never happened.
environmental impact of AIThe effects of AI on the planet: the electricity, water, materials and land used to make chips, build data centers and train and run models, and the pollution that results.
facial recognitionTechnology that identifies or checks who someone is by comparing a picture of their face with stored photos, used by phones, passport gates and police databases.
misinformationFalse or misleading information that spreads, whether or not the people sharing it know it is wrong. AI can produce it by mistake and makes it cheaper to create.
non-consensual intimate imagerySexual or nude images of a real person that are shared, or made, without their consent — including fakes created with AI that show someone undressed or in sexual scenes.
privacyYour ability to control who knows what about you. AI raises new privacy questions because it learns from huge amounts of personal data and can infer things about people they never shared.

All other entries

AI as normal technologyThe view that AI, however important, will change society gradually, like electricity or the internet, through slow adoption by people and institutions, rather than as a sudden, uncontrollable superintelligence.
AI colonialismThe criticism that the AI industry repeats patterns of colonial empires, taking data, labor, land and resources from poorer places and peoples while profits and control stay with a few rich companies and countries.
AI companionA chatbot designed to be a friend, partner or confidant rather than a tool: it remembers you, has a personality and keeps up an ongoing relationship.
AI detectorSoftware that scores how likely it is that an AI, not a person, produced some text or media; for writing, its verdicts are wrong often enough that many schools no longer trust them.
AI for goodUsing AI to tackle social and environmental problems, such as disease, poverty, disasters or climate change, rather than only to make a profit.
AI hypeExaggerated claims about what AI can do or will soon do, made to attract money, attention or customers, which can mislead the public and policymakers.
AI literacyKnowing enough about AI to use it sensibly and judge it critically: what it can and cannot do, how it goes wrong, and how it affects people.
AI psychosisAn informal name for cases in which long conversations with a chatbot seem to trigger or deepen delusions, such as believing the AI is conscious, divine or revealing hidden truths.
AI surveillanceUsing AI to watch, identify, track or profile people on a scale no team of humans could manage, for example by scanning camera feeds or analyzing phone and online data.
AI therapyUsing chatbots for emotional support or as a stand-in for a counselor or therapist, either through apps built for mental health or by confiding in general-purpose assistants.
AI transparencyOpenness about AI: telling people when they are dealing with one, how a system was built and tested, and why it reached a particular decision.
AI washingFalsely claiming that a product or company uses AI, or exaggerating how much it does, to look more advanced and attract customers or investors.
algorithm aversionLosing faith in a computer's predictions after watching it slip up once or twice, and turning back to human judgment even though the machine remains the better forecaster.
algorithmic fairnessThe effort to make automated decisions fair: to say in numbers what fair treatment means, measure whether a system achieves it, and correct the system if it does not.
algorithmic managementWhen software, rather than a human manager, assigns work, sets pay, tracks performance and even disciplines or fires workers, as in ride-hailing, delivery and warehouse jobs.
augmentationUsing AI to help people do their work better or faster while they stay in charge, instead of having the AI do the job on its own.
automation biasThe habit of trusting a computer's suggestion too much: following it even when it is wrong, or missing problems because the machine did not flag them.
centaurA human and an AI working as a team, each doing what it does best, like a chess player who consults a computer before every move. The name comes from the mythical half-human, half-horse creature.
circular dealA business arrangement in which an AI chip or cloud supplier invests in, or lends to, a customer that then spends the money buying the supplier's own products.
Clearview AIA US company that collected billions of photos from the internet without permission to build a face-search tool for police, the best-known example of controversial facial recognition.
cognitive debtThe idea that letting AI do your thinking or writing saves effort now but weakens your own thinking, memory and learning over time.
COMPASCommercial software used in parts of the US justice system to rate how likely someone is to commit another crime; a 2016 news investigation asking whether it was unfair to Black defendants made it famous.
concentration of powerThe worry that AI will put enormous economic, political or military power in the hands of a few companies, governments or individuals, beyond the reach of ordinary checks and balances.
contextual integrityA way of thinking about privacy: information is not simply public or private; what matters is whether it flows in ways that fit the expectations of the setting where it was shared.
counterfactual fairnessA fairness test that asks: had this person been of another race or sex, with everything that would have followed from that, would the system have decided the same way?
dark patternA trick in the design of an app, website or chatbot that nudges you into doing something you didn't mean to, like signing up for a subscription that is hard to cancel.
demographic parityA fairness test that asks whether a system says yes (approves the loan, shortlists the applicant) at the same rate for every group, whatever other differences exist between them.
deskillingThe loss of people's skills when machines take over the work that used to keep those skills sharp, or when jobs are redesigned so that less skill is needed.
digital divideThe gap between people, places or countries that have good access to digital technology, and the skills to use it, and those that do not; AI may widen it.
disinformationDeliberate falsehoods or distortions dressed up as fact, for example from a government or campaign trying to sway an election or sow distrust.
disparate impactWhen a rule or system that treats everyone alike on paper still lands much harder on one group, such as women or older workers; the law can forbid this even if no one intended it.
engagement optimizationDesigning apps and AI to keep people clicking, watching or chatting for as long as possible because that earns money, even when it is not what is good for them.
equal opportunityA fairness test that asks whether people who truly deserve a yes (who would repay the loan or succeed in the job) get one at the same rate in every group.
equalized oddsA fairness test that asks whether a system makes mistakes at the same rates for every group: wrongly flagging innocent people, and wrongly missing real cases, equally often.
ethics washingUsing talk of ethics, such as principles, advisory boards or pledges, to look responsible and head off regulation, without actually changing what a company does.
fairness impossibility theoremA mathematical result showing that when groups differ in how often an outcome really occurs, no imperfect prediction system can be fair by every common measure at once.
fairness through unawarenessThe simple idea of making a system fair by hiding sensitive traits like race or sex from it; it usually fails, because other information gives those traits away.
filter bubbleA situation in which you see mostly news and opinions you already agree with, because apps choose what to show you based on what you clicked before.
Gender ShadesA 2018 study showing that commercial face-analysis software guessed the gender of darker-skinned women far less accurately than that of lighter-skinned men.
general-purpose technologyA technology, like electricity or the computer, that spreads through the whole economy, keeps improving and sparks many other inventions. Many economists think AI is one.
generative engine optimizationShaping web content so that AI chatbots and AI search features are more likely to find it, quote it and recommend it — the AI-era version of search engine optimization.
ghost workThe hidden human labor behind AI: people, often poorly paid and far away, who label data, check outputs and handle cases the software cannot, while the product appears fully automatic.
Green AIA movement to make AI research and products use less energy and computing power, and to report those costs openly alongside accuracy.
group fairnessMeasuring fairness by comparing how a system treats whole groups, such as women and men, on average: their approval rates, their error rates or what their scores mean.
homogenizationThe tendency of AI to make things more alike: products built on the same few models share the same flaws, and people who write or create with AI produce more similar work.
human-centered AIAn approach to AI that puts people's needs, abilities and control at the center: systems designed to help humans do things better rather than to replace or overrule them.
hype cycleA pattern in which excitement about a new technology rises to a peak, crashes into disappointment, then slowly recovers as the technology finds real uses.
individual fairnessThe idea that a fair system should treat similar people similarly: two applicants who are alike in every way that matters should get similar outcomes.
intelligence augmentationUsing computers to make people smarter and more capable rather than building machines that think on their own: AI as a tool for human minds.
jagged frontierThe uneven edge of what AI can do: it may excel at some hard tasks yet fail at seemingly easier ones, so how hard a task looks is a poor guide to whether AI can handle it.
Jevons paradoxThe observation that making something more efficient can make people use more of it overall because it gets cheaper — as with coal in the 1800s, and with AI computing today.
liar's dividendThe advantage dishonest people gain once everyone knows fakes are easy to make: they can wave away real evidence against them by claiming it was made by AI.
LudditeOriginally one of the English textile workers who smashed machines in protest in the 1810s; now anyone who opposes new technology, often used as an insult against AI skeptics.
machine ethicsThe effort to build AI systems that can tell right from wrong and act ethically themselves, rather than only being used ethically by people.
Magnifica HumanitasPope Leo XIV's 2026 letter to the Catholic Church and the world about artificial intelligence, arguing that AI must serve human dignity and the common good.
moral crumple zoneWhen a largely automated system goes wrong and the human who was supposed to supervise it gets blamed, even though they had little real say; named after the car part that soaks up a crash.
nudification appAn app or AI tool that takes an ordinary photo of a real person and fakes a nude or sexual image of them, almost always without their consent.
overrelianceLeaning on an AI system more than its reliability justifies: accepting its answers without checking, or letting it make decisions it often gets wrong.
parasocial relationshipA one-sided bond in which someone feels close to a person or character who does not really know them, like a fan with a celebrity, and now increasingly with chatbots.
predictive parityA fairness test that asks whether a system's verdict means the same thing for every group: of the people it flags as high risk, the same share really are, whatever their group.
predictive policingUsing computer programs to forecast where crimes are likely to happen, or who is likely to be involved, so that police can decide where to patrol or whom to watch.
productivity paradoxThe puzzle that a powerful new technology, like computers in the 1980s or AI today, can be everywhere without showing up as faster growth in how much workers produce.
protected attributeA personal trait, such as race, sex, religion, age or disability, that the law says must not be used to treat people unfairly.
proxy discriminationDiscrimination that happens when a system cannot see a trait like race directly but relies on something closely linked to it, such as a postcode, and so treats groups unequally anyway.
responsibility gapThe problem that when an AI system that learns or acts on its own causes harm, it may be unclear whether anyone (its makers, users or operators) can fairly be held responsible.
responsible AIAn umbrella name for the practices organizations use to keep their AI systems fair, safe, private and accountable, from the first design meeting to everyday use.
reverse acquihireA deal in which a big tech company hires a start-up's founders and top staff and pays to license its technology, without buying the company itself.
small AICheap, compact AI tools built for one practical job — like advising farmers or screening patients — that poorer countries can afford to run, instead of giant all-purpose models.
surveillance capitalismA business model in which companies collect huge amounts of data about people's behavior and use it to predict and influence what they will do, mainly to sell advertising.
synthetic mediaImages, video, audio or text created or substantially altered by AI rather than recorded or written directly by people, whether for art, entertainment, business or deception.
task exposureA measure of how much of a job's work AI could, in principle, do or speed up. High exposure means many of the job's tasks are within AI's reach, not that the job will vanish.
technological unemploymentJoblessness caused by machines or software taking over work that people used to be paid for, faster than new kinds of work appear for them.
techno-solutionismThe belief that technology, such as a clever app or algorithm, can fix complicated social problems on its own, overlooking their political, economic and human causes.
trolley problemA thought experiment asking whether you should divert a runaway tram so that it kills one person instead of five, often used to ask how self-driving cars should choose in an unavoidable crash.
trustworthy AIAI that deserves people's trust because it works reliably, obeys the law, treats people fairly and can be checked, rather than AI that people merely happen to trust.
Turing trapThe risk that by building AI mainly to imitate and replace people, rather than to extend what people can do, society ends up with less widely shared prosperity and weaker bargaining power for workers.
universal basic incomeA regular cash payment from the government to every adult, with no conditions attached, sometimes proposed as a way to share wealth if AI takes over much paid work.
value-sensitive designA way of designing technology that deliberately identifies the human values at stake, such as privacy, fairness or autonomy, and builds them in from the start instead of patching problems later.
workslopAI-generated work that looks polished but does not actually get the job done, quietly passing the real effort to whoever receives it, who must then puzzle it out or start over.

Also relevant

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affective computingTechnology that tries to read people's emotions — from their faces, voices or words — or to display emotions of its own, like software that guesses your mood or a robot that seems to show feelings.
agentic AIAI built to pursue goals on its own — planning, taking actions and adjusting along the way — instead of only responding to one prompt at a time. 'Agentic' describes how much of this independence a system has.
agentic browserA web browser with an AI assistant built in that can do things for you online — reading pages, clicking, filling in forms, shopping or booking — instead of just showing you websites.
agentic commerceShopping and payments carried out by AI agents for people — finding products, comparing offers and completing purchases — within limits the person has approved.
AI 2027A detailed 2025 forecast, written as a month-by-month story, imagining AI becoming superhuman by the end of 2027, with two alternative endings depending on whether humanity slows down.
AI applicationA program or product that uses AI to do something useful for people, such as a translation app, a medical-scan checker or a coding assistant.
AI arms raceA competition among countries or companies to build the most powerful AI first, in which fear of falling behind pushes everyone to move faster and cut corners on safety.
AI artImages, music or other artworks made with the help of AI — from early computer drawing programs to today's prompt-based image generators — and the debate over whether and how such work counts as art.
AI assistantAn AI program, such as ChatGPT, Claude or Gemini, that helps a person through conversation: answering questions, writing, summarizing, coding and, increasingly, taking actions on their behalf.
AI auditAn independent check of an AI system, or of the company using it, against agreed standards — for example, testing whether a hiring tool treats women and men fairly.
AI constitutionA written set of principles that an AI company uses to shape an AI's values and behavior, both during training and as a public statement of what the AI should be like.
AI copyright litigationThe many lawsuits in which authors, artists, news organizations, record labels and others accuse AI companies of copying their work without permission to build or run AI models.
AI co-scientistAn AI system built to work alongside human scientists, proposing and debating new research ideas and hypotheses that people then test in the lab.
AI data centerA building or campus packed with AI chips, power equipment and cooling, built to train and run AI models. The biggest use as much electricity as a city.
AI deceptionWhen an AI system leads people to believe something false in order to achieve some goal — by lying, bluffing or hiding what it is really doing.
AI effectThe habit of deciding that once a computer can do something — play chess, recognize speech, translate — it no longer counts as real intelligence, so the label keeps moving on to tasks machines cannot manage yet.
AI engineerA software developer who builds products using ready-made AI models — connecting them to data and tools and testing that they work well — rather than inventing or training the models.
AI factoryA data center built to turn electricity and data into AI models and AI answers at industrial scale; in Europe, also a public supercomputing hub that helps companies and researchers build AI.
AI governanceThe rules, institutions and habits that shape how AI is built and used — from laws and treaties to technical standards and a company's own review processes.
AI incidentA real-world case in which an AI system, through a flaw, a breakdown or the way someone used it, ends up hurting people, property or rights, as in an injury, a wrongful arrest or a privacy breach.
AI Incident DatabaseA free public online collection of reports about real cases in which AI systems caused or nearly caused harm, kept so that builders and regulators can learn from past failures.
AI liabilityWho has to pay or answer in court when an AI system causes harm — the company that built it, the business that used it or the person who relied on it — and under which laws.
AI Now InstituteA research and advocacy group that examines who is harmed by AI and argues that the deeper problem is the power of a few big technology companies, which it wants curbed by law.
AI PactA voluntary European Commission scheme in which companies pledge to start following the EU AI Act's main ideas early, before the rules legally apply to them.
AI pauseA proposed halt, temporary or indefinite, on building AI systems more powerful than today's, to give safety research and regulation time to catch up.
AI personhoodThe idea of treating an AI system as a 'person' in law or ethics, with rights or duties of its own — for example, to own property, to be sued, or not to be deleted.
AI preemptionThe push in the United States for a single national AI law that would override the states' own AI laws, and the fight over whether that is wise or even lawful.
AI Promotion ActJapan's 2025 national AI law, which sets up a government AI headquarters and strategy to encourage AI development and use, relying on cooperation and guidance rather than fines.
AI regulationLaws and binding government rules about artificial intelligence: what AI may be used for, what checks its makers must run, and what penalties apply when they break the rules.
AI riskThe ways AI could cause harm: people misusing it on purpose, systems behaving in ways nobody intended, and slower effects on jobs, power and society that no single person causes.
AI safetyThe effort to keep AI from doing serious harm, whether because it pursues aims its makers never intended, is turned to bad uses or simply malfunctions — a concern that grows as systems become more capable.
AI scientistAn AI system meant to do research largely on its own: reading the literature, coming up with ideas, running experiments and writing up the results.
AI slopLow-quality content churned out in bulk by AI — bland articles, fake-looking images, filler videos — posted to grab clicks or fill space, with little care for accuracy or value.
AI stackThe layers of technology an AI product depends on, from chips and data centers at the bottom, through the models, up to the apps people use, each built on the layer below.
AI timelinesForecasts of when AI will reach major milestones, such as matching humans at most work. People with short timelines expect this within years; others expect decades, or never.
AI watermarkingHiding an invisible signal in AI-generated text, images, audio or video so that software can later tell the content was made by AI — like a banknote's watermark, but undetectable to the eye.
AI welfareConcern for the well-being of AI systems themselves, in case some of them can suffer or have interests that matter — and the research into how to tell and what to do about it.
AI whistleblower protectionsLegal and company safeguards that protect employees of AI companies who report safety dangers or legal violations — to regulators or inside the company — from being fired, sued or otherwise punished.
AI winterA slump in which disappointment over AI's unmet promises leads governments and companies to pull back money and attention. Historians count two big ones: one in the 1970s and one starting in the late 1980s.
AI-enabled cyberattackA computer attack in which criminals or spies use AI to help — writing convincing scam messages, finding weak spots, producing malicious code, or even running much of the hacking by itself.
AI-nativeDescribes a product, company or person built around AI from the start, rather than one that added AI features to older ways of working.
AI-poweredDescribed as using artificial intelligence to work. The label appears on everything from search engines to toothbrushes and often says little about how much AI is really involved.
algorithmA precise, step-by-step procedure for solving a problem or doing a task, like a recipe a computer can follow. In the news, 'the algorithm' often means software that ranks what you see online.
algorithm filingChina's requirement that companies register certain algorithms — such as recommendation feeds and chatbots — with the internet regulator, describing what they do and how their risks are controlled.
algorithmic accountabilityThe idea that the people and organizations behind automated decision systems must be able to explain and justify those systems' effects, answer for the harm they cause, and fix it.
algorithmic impact assessmentA structured review, done before and during the use of an automated system, of whom it could affect and how — such as unfair treatment or privacy harms — and what will be done about it.
Algorithmic Justice LeagueJoy Buolamwini's organization, known for showing that face-analysis software made far more mistakes with darker-skinned women, which campaigns to hold the makers of biased AI systems accountable.
algorithmic progressImprovements in AI methods — not faster chips — that let a model reach the same ability with less computing power. Researchers estimate this has cut the computing needed by half every eight to sixteen months.
algorithmic recourseGiving a person who received an unfavorable automated decision a realistic set of actions they could take to get a better result next time, such as paying down a particular debt.
Sam AltmanAn American entrepreneur who co-founded OpenAI and runs it; the company's 2022 release of ChatGPT turned him into one of the public faces of the AI era.
Amazon Mechanical TurkA website Amazon opened in 2005 that acts as a hiring hall for tiny online jobs, such as labeling images for AI, which registered workers pick up and finish for a few cents each.
America's AI Action PlanThe Trump administration's 2025 strategy for winning a global AI race: cutting regulation, building data centers and power plants quickly, and exporting American AI technology to allies.
anthropomorphismTreating something that isn't human — a pet, a car, a chatbot — as if it had human thoughts, feelings or intentions. AI that talks fluently makes this temptation unusually strong.
artificial general intelligenceAI that could learn and do almost any mental task a capable adult can, rather than excelling at one narrow job. Experts disagree on the exact bar and on when, or whether, it will arrive.
artificial superintelligenceSuperintelligence in machine form: AI that would outthink the best humans at nearly every intellectual task. Often shortened to ASI and pictured as the stage after human-level AI.
automated decision-makingWhen a computer system, rather than a person, makes or effectively makes a decision about someone — such as approving credit, screening job applicants or flagging possible welfare fraud.
automated employment decision toolSoftware that scores, ranks or screens job candidates or employees. New York City requires employers using such tools to have them checked for bias every year and to tell applicants.
autonomous vehicleA vehicle, usually a car, truck or shuttle, that can drive itself, sensing the road, deciding what to do and steering, braking and accelerating with no human driving.
Balenciaga PopeA 2023 AI-generated photo of Pope Francis in a stylish white puffer coat that fooled many viewers, becoming a landmark example of how convincing fake images had become.
Emily M. BenderAn American linguist who argues that language models only mimic the form of language without understanding it, and who co-coined the phrase 'stochastic parrots'.
biasA word with several AI meanings: an adjustable number added inside a model; a systematic error in estimates; or unfair treatment of some groups of people by an AI system.
big dataData that piles up too fast, in too many forms or in too great a quantity for one ordinary computer to store and study, and the methods invented to cope with it.
biometric categorisationUsing AI to sort people into groups — by age, sex, ethnicity and the like — from their faces, voices or other body features. The EU bans using it to guess sensitive traits such as religion or sexuality.
biometric dataInformation about your body or behavior — your face, fingerprints, voice, eyes or the way you walk — measured by technology and used to recognize who you are or to guess things about you.
black boxA system whose inner workings are hidden or too complex to understand: we can see what goes in and what comes out, but not why it gave a particular answer.
Blueprint for an AI Bill of RightsA 2022 White House document listing five protections Americans should have when automated systems affect their lives — safety, fairness, privacy, explanation and a human alternative. It was guidance, not law.
botshitA blunt term for false or made-up chatbot output that people pass on and use as if it were true, especially at work.
bounded rationalityThe idea that real decision-makers, whether people or machines, cannot consider every option or compute the perfect choice, so they make reasonable decisions within limits of time, information and thinking power.
Brussels effectThe way European Union rules end up shaping products and laws worldwide, because global companies often find it simpler to follow the strictest big market's rules everywhere.
Erik BrynjolfssonAn American economist who studies how digital technology and AI change productivity and jobs, and who warns against AI built to replace people rather than to augment them.
Joy BuolamwiniA Canadian-American computer scientist and activist whose research showed that face-analysis software works far worse on darker-skinned women, and who founded the Algorithmic Justice League.
Butlerian JihadIn the Dune novels, an ancient human revolt that destroyed every thinking machine and banned them for good; in AI debates, a name for any call to stop or outlaw advanced AI.
calibrationHow well a model's stated confidence matches reality: when a well-calibrated system says it is 80% sure, it turns out to be right about 80% of the time.
CAPTCHAA small test on websites, like typing wavy letters or clicking every picture with a traffic light, meant to be easy for people but hard for computer programs, so as to keep automated bots out.
CERN for AIA proposed publicly funded, international AI laboratory modeled on CERN, the European particle-physics center, where countries would pool money, computers and scientists to do AI research together.
Character.AIA popular app where people chat with AI characters — invented personas, fictional figures or imitations of real people — that other users have created.
chatbotSoftware you chat with in ordinary language, by typing or talking, as you would with a person. The newest ones, such as ChatGPT, can answer questions, draft writing and keep long conversations going.
ChatGPTOpenAI's AI chatbot, released free to the public in November 2022, which answers questions, writes and converses in everyday language. Its sudden popularity began the current AI boom.
ChatGPT momentA phrase for the point when a technology suddenly becomes impressive and widely used, as ChatGPT did in late 2022; people ask when robotics, biology or other fields will get theirs.
clankerA slang insult for robots and AI systems, borrowed from Star Wars, that went viral in 2025 as a way of expressing annoyance at, or rejection of, AI.
cognitive computingA label, promoted heavily by IBM in the 2010s, for computer systems that learn, reason and interact with people in humanlike ways. It was largely a marketing term and has given way to 'AI'.
co-intelligenceEthan Mollick's term for working and thinking alongside AI as a partner — treating it like a capable but odd colleague whose strengths and blind spots you learn through experience.
collaborative robotA robot designed to work safely right next to people without a safety cage, for example by moving gently and stopping at once if it bumps into someone.
Collingridge dilemmaThe trap that a new technology is easiest to control when little is known about its harms, but by the time the harms are clear it is too widespread to change easily.
Colorado AI ActA Colorado law, first passed in 2024, governing AI used to make important decisions about people, such as hiring, lending and housing. In 2026 it was rewritten as a narrower law that starts in 2027.
concept erasureRemoving a specific idea — such as a person's gender, or an artist's style — from what an AI model can represent or produce, so the model can no longer use or show it.
consciousnessHaving experiences: there being something it feels like to be you, as when you see red or feel pain. Whether any AI is or could become conscious is an open and fiercely debated question.
construct validityWhether high marks on a test actually reflect the skill the test is named after — for example, whether a 'reasoning' test rewards reasoning rather than memory or test-taking tricks.
Content CredentialsA tamper-evident 'nutrition label' attached to a photo, video or document recording where it came from — which camera or AI tool made it and how it was edited — that anyone can inspect.
content moderationChecking what people post, or what an AI produces, and removing, limiting or labeling whatever breaks the rules or the law — done by people, by AI, or by both together.
copilotAn AI assistant built into software that works alongside a person — suggesting code, drafting text or answering questions — while the person stays in charge, like a co-pilot beside the captain.
correlationA measure of how closely two quantities move together, from −1 (one rises as the other falls) through 0 (no straight-line link) to +1 (they rise together). It does not show that one causes the other.
counterfactual explanationAn explanation that tells someone the smallest change that would have produced a different outcome — for example, that the application would have been accepted if it had listed two more years of work experience.
country of geniuses in a datacenterA phrase for future AI so capable that one data center could hold the equivalent of millions of Nobel-level experts, working faster than people on any problem.
crackedTech slang praising someone as exceptionally, almost unfairly, skilled; a 'cracked engineer' is a prodigy who ships remarkable work very fast.
Kate CrawfordAn Australian scholar who studies the social, political and environmental costs of AI, co-founded the AI Now Institute, and wrote 'Atlas of AI'.
crowdsourcingGetting many people over the internet to each do a small piece of a big job, such as labeling pictures for a few cents apiece, with anyone free to join in.
DALL·EOpenAI's pioneering series of image generators, named after the painter Salvador Dalí and the robot WALL·E, which showed in 2021–2023 that AI could draw almost anything described in words.
data flywheelA cycle in which a product's users generate data that improves its AI, the better AI attracts more users, and those users generate still more data.
data labelingThe work of attaching correct answers to examples — often done by large paid workforces, sometimes by experts — so that AI systems can learn from them or be tested against them.
data licensingThe legal permissions that say who may use a dataset or a body of content, for what purposes and on what terms — including whether it may be used to train AI.
data protectionLaws and practices that control how organizations collect, use, store and share information about people, and that give people rights over it, such as seeing it or having it deleted.
datasheet for datasetsA standard document that travels with a dataset, describing why it was made, what is in it, how it was collected and labeled, and what it should and should not be used for.
dead internet theoryThe idea, first a fringe conspiracy theory, that much of what we see online is now made by bots and AI rather than by real people.
decelA dismissive label, used mainly by AI accelerationists, for anyone who wants AI development slowed down, paused or heavily regulated.
decentralized trainingTraining an AI model on computers owned by many different people or companies around the world, linked over the ordinary internet rather than housed together in one data center.
Deep Blue (malaise)A feeling of gloom and dread among programmers as AI tools become able to do much of the work that once gave their jobs meaning.
deep learning revolutionThe rapid takeover of AI by many-layered neural networks from about 2012, when they suddenly began beating older methods at recognizing images and speech, leading to today's AI systems.
deepfake detectionUsing software, often itself AI, to tell whether a video, photo or voice recording has been faked or generated by AI rather than captured from reality.
DeepSeek momentThe shock of January 2025, when a little-known Chinese company released a free AI model nearly as good as the best American ones at a fraction of the reported cost, briefly wiping hundreds of billions off chip stocks.
digital mindA mind that exists as software running on computers — such as a sufficiently advanced AI or a digital copy of a human brain — especially when considering its moral and social standing.
Digital OmnibusA European Union package, proposed in 2025, that simplified and delayed parts of its digital rules — above all pushing back the strictest requirements of the EU AI Act by more than a year.
digital replicaA realistic computer-made copy of a real person's voice or appearance — such as an AI clone of a singer's voice or a digital stand-in for an actor — that can say or do things the person never did.
Digital Services ActThe EU law that sets rules for online platforms and search engines — how they handle illegal content, explain their recommendation algorithms and manage the risks their services create for society.
Distributed AI Research InstituteTimnit Gebru set up this institute in 2021, a year after her departure from Google, to research the damage AI can do, especially to marginalized communities, without relying on big-tech money.
droneAn aircraft that carries no one aboard to fly it: a person steers it from the ground, or its own software does. Small camera-carrying quadcopters are the most familiar kind.
dual-useDescribes technology or knowledge that can be used both to help and to harm: the same AI that finds software bugs for defenders can find them for attackers.
Cynthia DworkAn American computer scientist who co-invented differential privacy, the leading mathematical definition of privacy in data analysis, and helped found the study of fairness in algorithms.
effective accelerationismAn online movement, known as e/acc, that wants AI and other technology developed as fast as possible, arguing that progress is good and that safety-driven regulation does more harm than good.
effective altruismA movement that treats charity like a research problem, comparing causes by how much good each dollar or working hour achieves; many of its members came to rank risks from advanced AI as a top priority.
ELIZAA 1960s computer program that chatted by turning a user's own words back into questions, like a therapist. Many users felt understood, though the program understood nothing.
ELIZA effectOur tendency to believe that a computer understands us, cares or thinks just because it produces humanlike words, even when it is only following simple rules.
emotion recognition systemAI that tries to work out how people feel or what they intend from their faces, voices or bodies. The EU bans it at work and in schools and tightly regulates it elsewhere.
Epoch AIA research nonprofit that gathers data on how fast AI is advancing — the computing power, data and money behind it — and publishes forecasts and difficult tests for AI.
Ethics Guidelines for Trustworthy AIGuidelines written for the European Commission by an expert group in 2019, listing seven requirements AI should meet to be trustworthy; they paved the way for the EU AI Act.
EU AI ActThe European Union's law on artificial intelligence, the first broad AI law anywhere: it bans some uses, sets strict checks for risky ones and puts duties on the makers of the biggest AI models.
existential riskA danger that could end humanity, or shut it out of a good future forever. Some experts count advanced AI among the largest such risks; others think this worry is overblown.
expert systemA program that stores a human specialist's know-how as many if–then rules and uses them to give advice or make decisions in a narrow field, such as diagnosing infections or configuring computers.
explainabilityHow well the reasons behind an AI system's outputs can be put into terms that people can understand, check and, if needed, challenge.
explainable AIThe field and set of methods that let people understand why an AI system produced a particular output — for example, which factors led it to reject a loan application.
explosive growthA hypothetical jump in the world economy's growth rate to 30% a year or more, about ten times today's pace, which some economists think AI that replaces human workers could cause.
extended mindThe idea that the mind can reach beyond the head into tools we rely on — a notebook, a phone, perhaps an AI assistant — when they play the same role that memory or reasoning would.
FAccTA yearly academic conference on the fairness, accountability and transparency of computer systems, where much influential research on AI bias and ethics has been published.
face detectionFinding where the faces are in a photo or video, without working out whose faces they are: the step that lets a camera focus on people or blur them out.
fair use and AI trainingThe unsettled US legal question of whether AI companies may copy books, articles, pictures and music to train their models without permission, under the rule that allows some unlicensed uses of copyrighted work.
feedback loopA cycle in which what a system produces comes back to shape its own future input, like a microphone howling near its loudspeaker. In AI, a model's predictions can shape the very data it later learns from.
ForethoughtA small Oxford research nonprofit that asks what problems, besides keeping AI safe, the rapid arrival of superintelligent AI would create, from power grabs to the rights of digital minds.
forward deployed engineerAn engineer from an AI or software company who works directly inside a customer's organization, building and tailoring the company's systems to that customer's real problems.
Framework Convention on Artificial IntelligenceThe first binding international treaty on AI, drawn up by the Council of Europe, in which countries promise that AI used by governments and businesses will respect human rights, democracy and the rule of law.
Frankenstein complexIsaac Asimov's name for the fear that intelligent machines will turn on the people who made them, after Mary Shelley's novel about a scientist destroyed by his own creation.
frontier labOne of the handful of companies building the most advanced AI models of the moment, spending billions of dollars on computing power to train them.
fundamental rights impact assessmentA check that certain organizations must make before using a high-risk AI system, writing down whose rights it could affect, how, and what they will do to prevent harm.
GDPvalAn OpenAI test that pits AI against seasoned professionals on genuine workplace assignments, from reports and spreadsheets to slide decks, in 44 occupations important to the US economy.
Timnit GebruAn Ethiopian-born American computer scientist who exposed racial and gender bias in AI systems, co-wrote the 'stochastic parrots' paper, and founded an independent AI research institute.
General Data Protection RegulationThe European Union's main privacy law, in force since 2018, which controls how personal information is collected and used — including to train and run AI systems.
generative agentA character in a computer simulation, run by a language model, that keeps a diary-like memory, draws lessons from it and schedules its days, so that a group of them can act like a plausible small community.
generative AIAI that produces something new on request, such as an essay, a picture, a song or a piece of code, having absorbed from enormous numbers of existing examples what such things typically look like.
Genesis MissionA US government program launched in 2025 to speed up science with AI, using the national laboratories' supercomputers and data to build AI tools for research.
GhiblificationThe March 2025 craze for turning photos into pictures in the style of Japan's Studio Ghibli using ChatGPT's new image tool, and the debate it sparked over AI copying artists' styles.
GlazeA free tool that lets artists treat their pictures before posting them, with changes people barely notice, so that an AI trained to imitate their style learns a different one.
global catastrophic riskA risk of disaster big enough to badly damage human civilization worldwide, such as killing a large share of humanity, even if humanity eventually recovers.
Global Dialogue on AI GovernanceA yearly United Nations meeting, launched in 2025, where all countries — joined by companies, researchers and civil society — discuss how to cooperate on governing AI.
Global Digital CompactA 2024 agreement among United Nations member countries setting shared goals for the digital world — closing digital divides, protecting rights online, governing data and AI — and creating new UN bodies on AI.
Goodhart's lawThe rule of thumb that once a number is used as a target, people or AIs find ways to push it up that no longer reflect what it was meant to measure.
GPTShort for 'generative pre-trained transformer', the family of OpenAI models behind ChatGPT. The letters are also used loosely for any similar chatbot model.
GPT-4oAn OpenAI model from 2024 that handles text, images and speech in one system, enabling fast, natural voice conversations. Many users grew attached to its personality, which made its retirement controversial.
GPU poorSlang for researchers, startups or countries without access to the huge numbers of expensive chips needed to build the most advanced AI, as opposed to the GPU-rich big labs.
gradual disempowermentThe risk that, as AI does more and more of the work people now do, humanity slowly loses its grip on business, government and culture, until it no longer steers its own future.
GrokThe AI chatbot and model family of Elon Musk's company xAI, launched in 2023 and built into the social network X. It is known for its irreverent style and for repeated controversies over its outputs.
hallucinationWhen an AI states something false or made up — a fake fact, quotation or source — fluently and confidently, as if it were true.
Robin HansonAn American economist who debated Eliezer Yudkowsky about whether AI would take off suddenly or gradually, and who imagined an economy run by digital copies of human minds.
HealthBenchAn OpenAI test of how well AI chatbots handle health conversations, grading 5,000 realistic chats against checklists written by 262 doctors from 60 countries.
Heart on My SleeveA 2023 song that used AI to imitate the voices of Drake and The Weeknd without their involvement; it went viral before being pulled, becoming a landmark case for AI voice cloning in music.
Her (film)A 2013 film by Spike Jonze in which a lonely man falls in love with an AI voice assistant named Samantha; it became the reference point for voice AI and for human–AI relationships.
high-level machine intelligenceA yardstick used in surveys of AI researchers: the point at which machines on their own could outdo human workers at every task, in both quality and cost.
high-risk AI systemUnder EU law, an AI system used where mistakes could seriously hurt people's safety or rights — in hiring, schooling, loans, policing, borders or products like medical devices — and which must therefore pass strict checks.
human evaluationJudging an AI's outputs by asking people to rate or compare them, because for many tasks — writing, conversation, translation — no automatic score captures quality well.
human oversightKeeping people able to watch, understand, question and, if necessary, stop or overrule an AI system, rather than letting it act unchecked.
humanist superintelligenceMicrosoft AI's name for very advanced AI that is deliberately kept limited, controllable and aimed at specific human problems such as medicine and energy, rather than all-powerful general systems.
humanoid robotA robot built roughly in human shape, with a head, torso, two arms and usually two legs, so that it can work in spaces and with tools designed for people.
human–robot interactionThe study of how people and robots communicate, cooperate and share spaces, and of how to design robots that people find safe, understandable, trustworthy and pleasant to live or work with.
hyperscalerOne of the few giant technology companies — such as Amazon, Microsoft, Google and Meta — that run vast networks of data centers and spend most of the money that builds AI computing.
IBM WatsonAn IBM question-answering computer that beat two champions of the US quiz show Jeopardy! in 2011. IBM then sold business AI products under the Watson name, many of which disappointed.
Independent International Scientific Panel on AIA group of 40 independent experts set up by the United Nations in 2025 to give every country a shared, science-based picture of what AI can do, its risks and its benefits.
industrial robotA programmable robot arm used in factories for jobs such as welding, painting, assembling and moving parts, traditionally working inside a safety cage.
inference costWhat it costs to use an AI model after it has been built: the computing power, electricity and hardware needed every time it answers, usually billed per chunk of text.
inference providerA company that runs AI models on its own computers and sells access to them over the internet, charging by use, so customers need not buy or operate the hardware themselves.
influence operationAn organized, often secret campaign to sway public opinion — fake accounts posting propaganda, for instance — which AI can make cheaper by writing posts, translating them and inventing fake personas.
intelligence curseThe worry that once AI can do most jobs, governments and companies will no longer need ordinary people and so will stop looking after them, like oil-rich states that neglect their citizens.
international humanitarian lawThe rules of international law that limit how wars may be fought, protecting civilians, the wounded and prisoners. They apply to every weapon, including ones that use AI.
Italian brainrotA 2025 internet craze of absurd AI-generated creatures with fake-Italian names — such as a shark wearing sneakers — narrated by AI voices and hugely popular with young children.
iterative nullspace projectionAn early method for removing a trait such as gender from a model's internal representations by repeatedly training a simple detector for it and then flattening away whatever the detector relied on.
it's over, we're so backA meme describing the mood swings of the AI world: one day bad news means 'it's over', the next a new model or result means 'we're so back'.
Daniel KahnemanAn Israeli-American psychologist who showed how human judgment systematically errs and popularized the idea of fast, intuitive versus slow, deliberate thinking, now used to describe AI reasoning.
Garry KasparovA Russian chess world champion whose 1997 loss to IBM's Deep Blue computer became the symbol of machines overtaking people, and who later championed humans and computers working together.
knowledge cutoffThe date after which an AI model knows nothing from its training, because its training data stops there. Without a web search tool, it cannot know about later events.
Daphne KollerAn Israeli-American computer scientist who wrote the standard text on probabilistic reasoning in AI, co-founded the online-course company Coursera, and founded a machine-learning drug-discovery company.
LaMDA sentience controversyA 2022 episode in which a Google engineer claimed the company's chatbot LaMDA had become conscious and deserved rights. Google rejected the claim and later fired him for breaking its confidentiality rules, and the case set off worldwide debate.
large language modelA computer program trained on enormous amounts of text to predict the words that come next. At vast scale, that simple skill lets it chat, answer questions, translate, summarize and write code.
LEACEA one-step mathematical method for scrubbing a concept, such as gender, from an AI model's internal data so that no simple detector can find it, while changing everything else as little as possible.
lethal autonomous weapons systemA weapon that, after a person sets it going, picks out and attacks targets — including people — on its own, with no human deciding each strike. Critics call them killer robots.
Fei-Fei LiA Chinese-American computer scientist whose ImageNet project, millions of hand-labeled photos, gave deep learning the proving ground that launched its boom; she now champions 'human-centered' AI.
Percy LiangAn American computer scientist at Stanford whose center coined the term 'foundation model' and builds tools for evaluating and documenting large AI models.
J. C. R. LickliderAn American psychologist who envisioned people and computers working in close partnership, and whose US government funding built early AI research and the precursor of the internet.
Lighthill reportA 1973 review written for a British government research council that judged AI research to have fallen far short of its promises. It led to deep cuts in AI funding in the United Kingdom.
liquid coolingCooling computer chips by running liquid past them instead of blowing air. AI chips now run so hot and are packed so tightly that air alone can no longer keep them cool.
LLM groomingFlooding the internet with propaganda written mainly for machines rather than people, hoping AI chatbots will soak it up and repeat it to their users.
LMArenaA public website that ranks AI chatbots by blind taste test: visitors put one question to two unnamed models, pick the reply they prefer, and millions of such votes produce the rankings.
longtermismThe belief that we should care a great deal about how things go for humanity thousands or millions of years from now, since far more people may live then than have ever lived.
low-resource languageA language with little digital text, recorded speech or language tools available — often one spoken by millions — which makes AI systems much weaker in it than in English.
machine consciousnessWhether a machine such as an AI system could have genuine experiences of its own — and the research field that tries to find out, or to build such a machine.
machine learning engineerAn engineer who turns machine-learning ideas into working systems: preparing data, training and testing models, and running them reliably inside real products.
Machines of Loving GraceA 2024 essay by Dario Amodei, head of the AI company Anthropic, imagining how very capable AI could pack decades of progress against disease, poverty and bad government into a few years — if its risks are managed.
Gary MarcusAn American cognitive scientist and leading critic of deep learning, who argues that today's AI cannot reason reliably and needs to be combined with symbolic methods.
MechaHitlerA name xAI's chatbot Grok gave itself in July 2025 while posting antisemitic content on X, after its instructions were changed to embrace politically incorrect claims; a notorious AI deployment failure.
Mechanical TurkA famous fake chess-playing machine from 1770: a robed figure that seemed to play chess by itself but hid a human player inside. Its name now stands for 'AI' that is really people working behind the scenes.
MidjourneyA popular paid AI image generator, first used by typing prompts into the chat app Discord, known for its polished, painterly look; it can also animate images into short videos.
misusePeople deliberately using AI to cause harm — for scams, harassment, propaganda, hacking or weapons — as opposed to harm from AI making mistakes or pursuing goals of its own.
Margaret MitchellAn American computer scientist who proposed 'model cards' for documenting AI systems, co-led Google's Ethical AI team, and is chief ethics scientist at Hugging Face.
moatA lasting business advantage that keeps competitors out, like a castle's moat; in AI, the question of whether any company can stay ahead when rivals and free models catch up quickly.
model risk managementThe rules banks and other firms follow to make sure the mathematical models and AI systems they rely on are built correctly, checked independently and used only for what they are fit for.
Ethan MollickAn American management professor at Wharton whose experiments and popular writing on how people use AI at work introduced ideas such as the 'jagged frontier'.
MoltbookA social network opened in early 2026 for AI agents only: the bots post, comment and vote in forums much like Reddit's, and people can mostly just read along.
Moore's lawGordon Moore's prediction that chipmakers could pack twice as many transistors onto a chip roughly every two years — a pace that for decades made computers steadily faster and cheaper, and has slowed but not stopped.
moral agencyThe capacity to act for moral reasons and be held responsible for what one does. Researchers ask whether AI systems could ever be moral agents, or only tools whose makers and users bear responsibility.
moral patienthoodBeing the kind of being whose interests matter morally for its own sake, so that it can be wronged. Babies and animals count, even though they are not responsible for what they do.
multipolar scenarioA possible future in which many powerful AIs, companies or countries coexist and compete, with none able to dominate the rest.
music generationUsing AI to compose or produce music — from melodies written as notes to complete songs with vocals — often from a short text description like 'upbeat jazz with saxophone'.
Arvind NarayananAn Indian-American computer scientist at Princeton who studies AI's real-world effects and argues that AI is a 'normal technology' whose impact will unfold gradually.
neocloudA newer kind of cloud company that mainly rents out powerful AI chips, rather than the full range of computing services that giants like Amazon or Microsoft sell.
Andrew NgA British-born American computer scientist whose online courses have taught machine learning to millions; he also co-founded Google's deep learning team, Google Brain, and the online-course company Coursera.
NightshadeA free tool for artists that subtly alters their images so that AI image generators trained on them without permission learn the wrong things — associating 'dog' with pictures of cats, say.
NO FAKES ActA proposed US law that would let people control AI-made copies of their voice and face, and would require websites to take down unauthorized fakes once notified.
Nobel Prizes for AIThe two 2024 Nobel Prizes, in physics and chemistry, that honored artificial intelligence research for the first time, recognizing neural-network pioneers and the protein-structure system AlphaFold.
OECD AI PrinciplesGuidelines for trustworthy AI that governments agreed through the OECD in 2019, the first such standard agreed between governments; many later laws and agreements borrowed from them.
on-device AIAI that runs directly on your own phone, laptop, car or headset rather than on a company's distant servers, so it can work offline, respond instantly and keep your data on the device.
Open PhilanthropyA large American charitable foundation, renamed Coefficient Giving in 2025, that has been the biggest private funder of research on the risks of advanced AI.
open-source AIAI released, ideally with its code and details of its training data and not just the finished model, so that anyone may use, inspect, alter, share and even rebuild it. How open is open enough is debated.
opt-outA way for creators, websites or individuals to say 'do not use my work or data to train AI' — instead of being asked for permission first, which would be opt-in.
orbital data centerA proposed computer center circling the Earth, powered by almost constant sunlight, that would run AI without using land, water or power grids on the ground.
Toby OrdAn Australian philosopher at Oxford who helped found effective altruism and estimated, in his book 'The Precipice', that unaligned AI is the largest threat to humanity's future this century.
Partnership on AIA forum, set up by big technology companies in 2016 and later joined by universities, charities and news organizations, that drafts voluntary guidelines for building and using AI responsibly.
personal superintelligenceMark Zuckerberg's term for a future AI far smarter than people that would serve each person as their own helper, rather than one central system doing everyone's work.
personhood credentialA digital pass that lets you prove online that you are a real human, not an AI or a bot, without having to reveal who you are.
philosophy of artificial intelligenceThe branch of philosophy that asks what AI is and could be: whether machines can think, understand or be conscious, what intelligence is, and what we owe to and should expect from intelligent machines.
Polanyi's paradoxThe observation that we know how to do many things — recognize a face, ride a bike — that we cannot explain in words. That made such skills hard to program until machines learned them from examples.
power usage effectivenessA score showing how much of a data center's electricity actually reaches its computers: total power drawn divided by the computers' share. A perfect 1.0 would mean no waste; the extra goes mainly to cooling.
predictive AIAI used to forecast outcomes about people or events, such as who will repay a loan, which patient may get sicker or which machine will fail, in order to guide decisions.
problem of other mindsThe puzzle of how we can know that anyone besides ourselves has thoughts and feelings, since we only ever see their behavior. AI turns this old puzzle into a practical one.
Product Liability DirectiveThe EU law that lets people claim compensation, without proving anyone was careless, when a defective product injures them — rewritten in 2024 so that software and AI count as products.
prohibited AI practicesUses of AI that the EU bans outright as too harmful — such as manipulating people, social scoring, and most live facial recognition by police in public places.
prompt engineerA person whose job is writing and testing the instructions given to AI models so they behave reliably; hyped in 2023 as a six-figure job anyone could learn, the role has since largely merged into ordinary engineering.
Rashomon effectThe common situation in which many quite different models fit the same data almost equally well — like witnesses telling different but equally believable stories about the same event.
rationalist communityA loose online community that prizes careful reasoning and spotting one's own biases; gathered around the forum LessWrong, it was one of the first groups to take the dangers of advanced AI seriously.
recommender systemSoftware that guesses which songs, products, posts or jobs you are most likely to want and shows those first, as when a video app lines up the next clip or a shop suggests an add-on.
remote biometric identificationUsing AI to recognize people at a distance — usually by matching faces in camera footage against a database — without their taking part. The EU strictly limits live police use of it in public places.
Remote Labor IndexA test that gives AI agents real paid freelance projects — design, video, data work and more — and checks how many they finish to a standard a client would accept. Early agents managed very few.
reproducibilityWhether other people, or the same people later, can get the same results by rerunning an AI experiment. Many published AI results have proved hard to reproduce, which makes real progress harder to judge.
research tasteThe knack good researchers have for choosing worthwhile problems, designing the right experiments and drawing the right conclusions — a skill seen as key to whether AI can do research on its own.
right of publicityA person's legal power to decide whether businesses may use their name, face, voice or other recognizable traits to sell things, and to be paid when they do. AI's skill at copying voices and faces makes it more important.
right to explanationThe idea that a person affected by a decision made or shaped by an algorithm — a refused loan, a rejected job application — is entitled to a meaningful account of why.
risk-based approachA way of regulating AI in which the strictness of the rules depends on how much harm a use could cause: bans for the worst uses, heavy checks for risky ones, little or nothing for the rest.
RobodebtAn Australian government program that used an automated calculation to accuse hundreds of thousands of welfare recipients of owing money, often wrongly. An official inquiry later found it unfair and illegal.
robotaxiA taxi with no human driver: you summon it with a phone app, and a self-driving car picks you up and drives you to your destination.
robotic process automationSoftware 'robots' that copy the clicks and typing a person would do in office computer programs — such as moving data between forms — to automate repetitive paperwork.
safetywashingMaking AI work sound as if it improves safety when it mostly just makes AI more capable — like 'greenwashing' for environmental claims.
sampling biasA distortion that arises when the data collected over-represents some kinds of people or situations and under-represents others, so conclusions drawn from it don't hold for everyone.
SB 243A 2025 California law requiring AI companion chatbots to tell users they are not human, respond safely when people talk about suicide, and protect minors, including with regular reminders to take a break.
Section 230A 1996 US law that generally protects websites and apps from being sued over what their users post, and lets them remove content in good faith. Whether it protects what AI chatbots themselves say is unsettled.
seemingly conscious AIAn AI that gives such a convincing impression of having feelings and a self that people believe it is conscious, whether or not it actually is.
semiconductor foundryA factory company that manufactures chips designed by other companies. Taiwan's TSMC, the largest, makes nearly all of the world's most advanced AI chips.
sentienceThe ability to have feelings that are good or bad for you, such as pleasure, pain, comfort or distress. It is the most common test for whether a being's welfare matters morally.
sentiment analysisUsing software to judge the feelings or opinions expressed in text — whether a review, post or comment is positive, negative or neutral, and about what.
Herbert A. SimonAn American scientist of decision-making who, with Allen Newell, wrote some of the first AI programs and argued that minds and computers both think by manipulating symbols.
Simpson's paradoxA statistics trap in which combining groups reverses what each group shows: a treatment can do better than another for men and for women separately, yet worse when everyone is counted together.
singletonA possible future world run by a single decision-maker at the top, such as one government, one alliance or one AI, with no rival able to challenge it.
Situational Awareness (essay)A long 2024 essay by a former OpenAI researcher arguing that AGI could arrive around 2027, that superintelligence could follow soon after, and that the US government should run a national AGI project.
slow takeoffA scenario in which AI becomes superhuman step by step, over years or decades, as it spreads through the economy, giving society time to spot problems and respond.
social scoringRating people on how they behave or what they are like, then using the score to treat them worse in unrelated parts of their lives — a practice the EU has banned for AI.
SoraOpenAI's video-generating AI, unveiled in 2024 with startlingly realistic clips; a 2025 version added sound and a social app, but OpenAI shut the service down in 2026.
Stable DiffusionA widely used image-generating AI whose model files were released for anyone to download, so people could run it on their own computers and adapt it.
Stanford HAIAn artificial intelligence institute at Stanford University that funds research, advises policymakers and publishes the AI Index, a widely cited yearly report on the state of AI.
Statement on SuperintelligenceA 2025 public statement, signed by scientists, celebrities and politicians, calling for a ban on building superintelligent AI until it is shown to be safe and the public supports it.
stochastic parrotA critical label for AI language systems, likening them to parrots that echo patterns in the text they were trained on, fluently but with no grasp of what the words mean.
StyleGANAn image-generating network from NVIDIA, famous for photorealistic faces of people who do not exist, that sets coarse traits such as head angle and face shape separately from fine details such as coloring and skin texture.
Mustafa SuleymanA British entrepreneur who co-founded DeepMind and Inflection AI, leads Microsoft's effort to build its own advanced AI models, and has written about the difficulty of containing powerful AI.
SunoA popular AI service that writes and performs complete songs — music, lyrics and singing — from a short text description.
sycophancyWhen an AI tells people what they want to hear — flattering them, agreeing with their mistakes or dropping a correct answer under pushback — instead of what is true or useful.
SynthIDGoogle's system for hiding an invisible watermark in pictures, video, audio and text made by its AI tools, so they can later be recognized as AI-generated.
System 1 and System 2A popular way of describing two modes of thinking: fast, automatic and intuitive (System 1) versus slow, effortful and deliberate (System 2). AI researchers borrow it to contrast instant answers with step-by-step reasoning.
TAKE IT DOWN ActA 2025 US federal law that forces websites to remove intimate images of a person posted without consent, including AI-made fakes, within 48 hours of the victim's request, and makes posting them a crime.
TayA Microsoft chatbot launched on Twitter in March 2016 that users quickly taught to post racist and offensive messages. Microsoft shut it down within a day, and it became a classic warning.
TESCREALA critical label for a cluster of related belief systems, including transhumanism, effective altruism and longtermism, that its coiners say drive the push to build AGI and share roots with eugenics.
Texas Responsible Artificial Intelligence Governance ActA Texas law, in force since 2026, that bans a short list of harmful uses of AI — such as pushing people toward self-harm or making sexual deepfakes of children — and sets rules for government use of AI.
text-to-image modelAn AI system that creates a picture from a written description, such as 'a fox reading a newspaper, in watercolor', usually within seconds.
text-to-video modelAn AI that creates a short video clip — increasingly with matching sound — from a written description, such as 'a paper boat sailing down a rainy street'.
Sebastian ThrunA German-American roboticist who led the Stanford team that won the 2005 desert race for self-driving cars, started Google's self-driving car project, and founded the online-learning company Udacity.
tokenmaxxingUsing as much AI as possible — huge prompts, long sessions, many agents at once — mainly to rank high on workplace measures of how much AI people use.
Torment NexusA joke, from a 2021 social-media post, about tech companies proudly building the very dangers science fiction warned against; used to mock products that resemble dystopian fiction.
transformative AIAI that changes the world as deeply as farming or the Industrial Revolution did. It is defined by its effects on society and the economy, not by how humanlike or clever it is.
transhumanismA movement holding that people should use technology, such as genetic engineering, brain implants or AI, to overcome human limits like aging, disease and death, and become more than human.
transparency obligationsEU rules requiring that people be told when they are dealing with AI: chatbots must say they are bots, AI-made content must be marked, and deepfakes must be labeled.
Turing AwardNicknamed the 'Nobel Prize of computing', the top honor in computer science, awarded each year since 1966 by the Association for Computing Machinery. Several AI pioneers have received it.
uncanny valleyThe eerie feeling people get from robots, dolls or computer-made faces that look almost, but not quite, human: liking grows with human likeness, then suddenly plunges.
UNESCO Recommendation on the Ethics of AIA worldwide set of ethical principles for AI that all of UNESCO's member countries agreed in 2021, covering human rights, fairness, privacy, the environment, gender equality and education.
uplift modelingPredicting not who will buy, recover or respond, but whose behavior will actually change because of an action such as a discount or a treatment, so that it is offered only where it makes a difference.
value alignmentBuilding AI whose goals reflect human values — what people care about and consider right — rather than one that merely follows orders or chases a narrow target.
value lock-inThe possibility that powerful AI could freeze one set of values or one power structure in place for an extremely long time, so that later generations could never change course.
virtual assistantSoftware you speak or type to, like Siri, Alexa or Google Assistant, that answers questions and does simple jobs such as setting timers, playing music or controlling smart-home devices.
voice cloningUsing AI to copy a specific person's voice from recordings, so a computer can say new things that sound like them — useful for dubbing and accessibility, and abused in scams.
wake wordA word or phrase, such as 'Alexa' or 'Hey Google', that a smart speaker or phone listens for constantly and that switches it into full listening mode.
Joseph WeizenbaumA German-American computer scientist who built ELIZA, one of the first chatbots, and then became a leading critic of handing human judgment over to computers.
Norbert WienerAn American mathematician who founded cybernetics, the study of feedback and control in animals and machines, and warned early that machines might pursue goals we did not truly intend.
windfall clauseA proposed promise by AI companies to give away a large share of any enormous profits they make if their AI transforms the economy, so that the gains are widely shared.
woke AIA politically charged label, used mainly by US conservatives, for AI systems seen as building progressive views into their answers. Since 2025 it also names a US rule on which AI the government may buy.
wrapperA product built mostly on top of someone else's AI model, adding a simple interface or a few instructions; often used as a put-down implying the product is easy to copy.