A Severe Misalignment of AI in Mathematics
A Severe Misalignment of AI in Mathematics is a declaration about artificial intelligence and mathematical research, published on September 11, 2026 at mathandai.org with 25 initial signatories
AI Ethics
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AI Alignment
AI alignment is the study and practice of making artificial intelligence systems behave in ways that accord with intended goals, preferences, constraints, or institutions. The term is used at several levels.
AI Anxiety
The term "Artificial Intelligence" (AI) anxiety describes the fear, stress, distress, and trepidation that people feel in response to the development and increasing role of artificial intelligence in daily…
AI Fairness 360 (AIF360)
AI Fairness 360, abbreviated AIF360, is an open-source Python and R toolkit, originally created by IBM Research and released in September 2018, that detects and mitigates unwanted bias in machine learning…
AI and religion
AI and religion refers to the use of artificial intelligence within religious life and to the responses that faith traditions have given to AI.
AI bias
AI bias (also called algorithmic bias) is systematic, repeatable error in artificial intelligence systems that produces unfair, discriminatory, or skewed outcomes, typically disadvantaging groups defined by…
AI consciousness
AI consciousness refers to the ongoing scientific and philosophical debate about whether artificial intelligence systems can possess
AI ethics
AI ethics is the field that studies the moral principles, values, and frameworks governing how artificial intelligence systems are designed, built, deployed, and used, and the obligations that developers and…
AI regulation
AI regulation is the body of laws, binding rules, technical standards, and government enforcement mechanisms that oversee how artificial intelligence systems are built, sold, and used.
AI safety
AI safety is the research and practice of preventing or reducing unacceptable harm from artificial intelligence systems.
AI-generated content
AI-generated content (also called AIGC or synthetic media) is text, images, video, audio, music, or code produced wholly or partly by artificial intelligence systems rather than by a human author.
Algorithmic bias
Algorithmic bias is the tendency of a computer system to produce systematic, repeatable errors that advantage some groups of people over others.
Algorithmic fairness
Algorithmic fairness is the study of how automated decision systems can be made to produce decisions that are equitable across protected attributes such as race, gender, age, religion, and disability.
Amanda Askell
Amanda Askell is a Scottish philosopher and artificial intelligence researcher who works on fine-tuning and alignment at Anthropic, where she leads the team responsible for the character, persona, and values…
Amazon Mechanical Turk
Amazon Mechanical Turk (MTurk) is a crowdsourcing marketplace operated by Amazon in which businesses and researchers ("requesters") post small paid tasks that a distributed pool of workers completes over the…
Anthropic Long-Term Benefit Trust
The Anthropic Long-Term Benefit Trust (LTBT) is a Delaware purpose trust that holds a special class of Anthropic stock and uses it to elect a portion of the company's board of directors.
Automation Bias
Automation bias is the tendency for humans to favor suggestions and outputs from automated decision-making systems over contradictory information from non-automated sources
Autonomous weapons
Autonomous weapons, usually discussed under the label lethal autonomous weapon systems (LAWS), are weapon systems that, once activated, can select and engage targets without further intervention by a human…
BBQ (Bias Benchmark for QA)
BBQ (the Bias Benchmark for QA) is a hand-built evaluation dataset that measures whether a question answering (QA) language model relies on social stereotypes when it answers.
Bias
Bias in artificial intelligence carries three distinct technical meanings: a learnable scalar parameter added inside a neuron, the systematic error component of an estimator (the "bias" in the bias-variance…
Blueprint for an AI Bill of Rights
The Blueprint for an AI Bill of Rights is a non-binding policy framework released by the White House Office of Science and Technology Policy (OSTP) on October 4, 2022, during the Biden-Harris administration.
BookCorpus
BookCorpus (also written BooksCorpus, and sometimes called the Toronto Book Corpus) is a text dataset built from free, self-published English-language ebooks scraped from the distribution platform Smashwords.
C2PA (Coalition for Content Provenance and Authenticity)
The Coalition for Content Provenance and Authenticity (C2PA) is an open technical standards body that develops Content Credentials, a cryptographically signed metadata format for tracking the provenance and…
COMPAS (recidivism risk assessment)
COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is a proprietary actuarial risk-assessment instrument used by United States courts and corrections agencies to estimate the…
Charity
Charity, nonprofit, and humanitarian work has become one of the most visible testbeds for applied artificial intelligence.
Collective Constitutional AI
Collective Constitutional AI (CCAI) is a 2023 research project by Anthropic and the Collective Intelligence Project (CIP) that sourced the value principles
Confirmation Bias
Confirmation bias is the tendency to search for, interpret, favor, and recall information in ways that confirm one's preexisting beliefs, and in artificial intelligence it appears in three main forms: human…
Content Authenticity Initiative
The Content Authenticity Initiative (CAI) is an Adobe-led community and implementation effort for attaching verifiable origin and editing information to digital content.
Content provenance
Content provenance is the set of techniques, standards, and policies for recording and disclosing the origin, authorship, and edit history of digital media.
Copyright and AI
Copyright and AI is the body of law, agency practice, and litigation that governs how copyright applies to systems that learn from existing works and produce new material. Two questions dominate the field.
Counterfactual Fairness
Counterfactual fairness is a formal definition of algorithmic fairness rooted in causal inference: a prediction is counterfactually fair toward an individual if it would remain unchanged in a counterfactual…
Coverage Bias
Coverage bias is a type of selection bias that occurs when the method used to collect data systematically excludes part of the target population
Deepfake
A deepfake is synthetic media in which a real person's face, voice, or body is digitally replaced, manipulated, or fabricated using artificial intelligence, most often deep learning techniques such as…
Demographic Parity
Demographic parity, also called statistical parity or acceptance rate parity, is a fairness criterion in machine learning that requires a model's predictions to be statistically independent of a protected…
Differential privacy
Differential privacy is a mathematical definition of privacy that guarantees the output of an analysis is essentially unchanged whether or not any single individual's record is included in the input
Disparate Impact
Disparate impact is a legal and statistical concept describing situations where a seemingly neutral policy, practice, or algorithm produces disproportionately adverse outcomes for members of a protected class…
Disparate Treatment
Disparate treatment is the intentional, less favorable treatment of an individual because of a protected attribute such as race, gender, age, religion, national origin, or disability, and in machine learning…
ELIZA effect
The ELIZA effect is the tendency of people to attribute understanding, empathy, and other human mental states to a computer program on the strength of its conversational output
Effective Altruism
Effective altruism (often abbreviated EA) is a philosophical and social movement that uses evidence and careful reasoning to identify the most effective ways to benefit others
Effective accelerationism
Effective accelerationism (often abbreviated e/acc) is a techno-optimist ideological movement that advocates for the maximum acceleration of technological development, particularly artificial intelligence.
Emily M. Bender
Emily M. Bender is an American linguist and a professor in the Department of Linguistics at the University of Washington, where she directs the Computational Linguistics Laboratory.
Equality of Opportunity
Equality of opportunity is a group-fairness criterion in machine learning that requires a classifier's true positive rate (TPR) to be equal across all groups defined by a sensitive attribute: qualified…
Equalized Odds
Equalized odds is a group fairness criterion in machine learning that requires a classifier's true positive rate (TPR) and false positive rate (FPR) to be equal across all groups defined by a protected…
Existential risk from AI
Existential risk from artificial intelligence (also called AI x-risk) is the hypothesis that the development of sufficiently advanced artificial intelligence could cause human extinction, permanent…
Experimenter's Bias
Experimenter's bias (also called the observer-expectancy effect, experimenter expectancy effect, or experimenter effect) is a type of cognitive bias in which a researcher's expectations or beliefs about the…
Explainable AI
Explainable AI (XAI) refers to artificial intelligence systems and techniques designed so that humans can understand how and why the system reaches its decisions, predictions, or recommendations.
Facial Recognition
Facial recognition is a biometric technology that identifies or verifies the identity of an individual by analyzing patterns in a digital image or video frame of the person's face.
Fair use
Fair use is a limitation on the exclusive rights of copyright owners in United States law, codified at 17 U.S.C. 107.
Fairlearn
Fairlearn is an open-source Python toolkit for assessing and improving the fairness of machine-learning models with respect to sensitive attributes such as race, gender, or age.
Fairness Constraint
A fairness constraint is an explicit mathematical condition imposed on a machine learning model during training, evaluation, or post-processing that forces its predictions to satisfy a specified group-fairness…
Fairness Metric
A fairness metric is a quantitative, mathematical measure used to evaluate whether a machine learning model's predictions or decisions treat different demographic groups equitably.
Federated Learning
Federated learning is a machine learning technique that trains a shared model across many decentralized devices or servers without moving their raw data to a central location.
Feedback Loop
A feedback loop in machine learning is a cycle in which a deployed model's predictions influence the real world, and the resulting data is then collected and used to retrain the same model
General Data Protection Regulation (GDPR)
The General Data Protection Regulation (GDPR), formally Regulation (EU) 2016/679, is the European Union law that governs how the personal data of individuals in the EU and European Economic Area (EEA) is…
Group Attribution Bias
Group attribution bias is the tendency to assume that what is true of one member of a group is true of the entire group
Human-in-the-loop
Human-in-the-loop (HITL) describes any arrangement in which a person is a required participant in an automated system's operating cycle rather than a bystander to it.
Implicit Bias
Implicit bias is an umbrella term that, in artificial intelligence and machine learning, refers to systematic tendencies operating below the surface of explicit design choices.
In-Group Bias
In-group bias (also called in-group favoritism or in-group preference) is the systematic tendency to favor members of one's own social group over members of other groups
Incompatibility of Fairness Metrics
The incompatibility of fairness metrics (also called the impossibility theorem of fairness or fairness trade-offs) is the proven mathematical result that several widely used definitions of algorithmic fairness…
Individual Fairness
Individual fairness is the principle in machine learning that any two individuals who are similar with respect to a task should receive similar algorithmic outcomes.