Showing 1-60 of 74 articles
AI Alignment
AI alignment is the field of AI research dedicated to ensuring that artificial intelligence systems pursue the goals, values, and intentions their human...
AI SafetyMachine Learning
AI Anxiety
The term "Artificial Intelligence" (AI) anxiety describes the fear, stress, distress, and trepidation that people feel in response to the development and...
Artificial Intelligence
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...
AI Tools & ProductsOpen Source AI
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. Since the...
Artificial Intelligence
AI bias
AI bias (also called algorithmic bias) is systematic, repeatable error in artificial intelligence systems that produces unfair, discriminatory, or skewed...
AI SafetyArtificial Intelligence
AI consciousness
See also: Artificial intelligence, AI ethics, AI safety, Large language model AI consciousness refers to the ongoing scientific and philosophical debate about...
AI SafetyArtificial Intelligence
AI ethics
AI ethics is the field that studies the moral principles, values, and frameworks governing how artificial intelligence systems are designed, built, deployed,...
AI SafetyArtificial Intelligence
AI regulation
AI regulation is the body of laws, binding rules, technical standards, and government enforcement mechanisms that oversee how artificial intelligence systems...
AI SafetyArtificial Intelligence
AI safety
AI safety is a multidisciplinary field of research and practice focused on ensuring that artificial intelligence systems operate in ways that are beneficial,...
AI SafetyArtificial Intelligence
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...
Artificial Intelligence
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...
AI SafetyMachine Learning
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...
AI SafetyPeople
Automation Bias
Automation bias is the tendency for humans to favor suggestions and outputs from automated decision-making systems over contradictory information from...
Machine Learning
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...
AI BenchmarksAI Safety
Bias
Bias in artificial intelligence carries three distinct technical meanings: a learnable scalar parameter added inside a neuron, the systematic error component...
Machine LearningNeural Networks
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...
AI Policy & Regulation
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...
AI Policy & RegulationGenerative AI
COMPAS (recidivism risk assessment)
COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) is a proprietary actuarial risk-assessment instrument used by United States...
Charity
See also: Charity ChatGPT Plugins Charity, nonprofit, and humanitarian work has become one of the most visible testbeds for applied artificial intelligence....
AI Tools & ProductsChatGPT
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, or...
AI AlignmentAnthropic
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...
AI SafetyData Science
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....
AI Policy & RegulationGenerative AI
Counterfactual Fairness
Counterfactual fairness is a formal definition of algorithmic fairness rooted in causal inference: a prediction is counterfactually fair toward an individual...
Machine LearningStatistics
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, so the sample...
Data & DatasetsMachine Learning
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,...
Artificial IntelligenceComputer Vision
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...
Machine Learning
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...
Computer ScienceStatistics
Disparate Impact
Disparate impact is a legal and statistical concept describing situations where a seemingly neutral policy, practice, or algorithm produces disproportionately...
AI Policy & RegulationMachine Learning
Disparate Treatment
Disparate treatment is the intentional, less favorable treatment of an individual because of a protected attribute such as race, gender, age, religion,...
AI Policy & RegulationMachine Learning
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...
AI Safety
Effective accelerationism
Effective accelerationism (often abbreviated e/acc) is a techno-optimist ideological movement that advocates for the maximum acceleration of technological...
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...
Natural Language ProcessingPeople
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...
Machine Learning
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...
Machine Learning
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...
AI SafetyArtificial Intelligence
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...
Machine LearningStatistics
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...
Interpretability
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...
AI Tools & ProductsComputer Vision
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...
AI Tools & ProductsMicrosoft
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...
Machine Learning
Fairness Metric
A fairness metric is a quantitative, mathematical measure used to evaluate whether a machine learning model's predictions or decisions treat different...
Machine LearningModel Evaluation
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...
Deep LearningMachine Learning
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...
Machine Learning
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...
AI Policy & Regulation
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, or that a group's collective decision...
Machine Learning
Implicit Bias
Implicit bias is an umbrella term that, in artificial intelligence and machine learning, refers to systematic tendencies operating below the surface of...
Machine Learning
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...
Machine Learning
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...
Machine Learning
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...
Machine Learning
Interpretability
Interpretability in artificial intelligence is the degree to which a human can understand the cause of a decision a machine learning model makes, by inspecting...
Machine LearningModel Evaluation
Joy Buolamwini
Joy Buolamwini is a Canadian-American computer scientist and digital activist known for research exposing racial and gender bias in commercial facial...
Computer VisionPeople
MACHIAVELLI (benchmark)
MACHIAVELLI is a benchmark for evaluating the ethical behavior of AI agents in text-based interactive environments. Introduced in 2023 by Alexander Pan, Jun...
AI AlignmentAI Benchmarks
Machine learning terms/Fairness
The key machine learning fairness terms are the formal criteria used to define and measure when a model treats demographic groups equitably, together with the...
Artificial IntelligenceMachine Learning
Margaret Mitchell (computer scientist)
Margaret Mitchell is an American computer scientist who works on AI ethics, fairness in machine learning, and the documentation of AI systems. She is best...
Machine LearningPeople
Max Tegmark
Max Tegmark is a Swedish-American physicist and artificial intelligence researcher who is a professor of physics at the Massachusetts Institute of Technology...
AI SafetyPeople
Meredith Whittaker
Meredith Whittaker is an American technologist, researcher, and privacy advocate who serves as president of the Signal Foundation, the nonprofit behind the...
AI SafetyPeople
Model card
A model card is a short, standardized document that accompanies a trained machine learning model and reports its intended use, training data, evaluation...
Developer Tools
Model welfare
Model welfare is the research area that investigates whether advanced AI systems might have morally relevant experiences or interests, such as suffering or...
AI Safety
Nick Bostrom
Nick Bostrom (born Niklas Boström, 10 March 1973) is a Swedish-born philosopher best known for the 2014 book Superintelligence: Paths, Dangers, Strategies, the...
AI SafetyPeople
Out-Group Homogeneity Bias
Out-group homogeneity bias, also called the out-group homogeneity effect, is the cognitive bias in which people perceive members of an out-group as more...
Machine Learning