Ecological Fallacy Bias - The ecological fallacy is the mistake of treating a relationship or characteristic observed for groups as if it necessarily held for individuals within...
Edge case - An edge case is an input or situation near the limits of a system's assumptions, rules, or operating conditions, or an unusual combination that challenges...
Emergent Bias - Emergent bias is systematic unfairness that becomes apparent through the interaction of a computer system with its real context of use, particularly as...
Empirical risk - Empirical risk is a model's average loss on a specified collection of observed examples.
Empirical risk minimization - Empirical risk minimization, or ERM, is the learning principle of choosing a model from an allowed set to minimize average loss on the training examples.
Emulation - Emulation is the reproduction of relevant behavior of one system by another system so that software, users, or connected components can interact with the...
Equality of opportunity - In binary machine-learning fairness, equality of opportunity usually means that the true-positive rate is the same across the groups being compared.
Equalized odds - Equalized odds is a statistical fairness criterion requiring a model's predictions to be independent of group membership once the actual target outcome is...
Error propagation - Error propagation is the way an error or uncertainty in one part of a process affects later calculations, predictions, or decisions.
Ethics by design - Ethics by design is an approach that incorporates ethical considerations into the purpose, requirements, development, deployment, and ongoing management of...
Ethics of artificial intelligence - Ethics of artificial intelligence is the study and practice of evaluating what ought to be done when AI is designed, developed, deployed, and used.
Evaluation Bias - Evaluation bias arises when the data, benchmarks, measurements, or assessment procedures used to judge an AI system do not support an accurate picture of...
Evaluation machine learning pipeline - An evaluation machine learning pipeline, in the ITU telecommunications context, connects ML pipeline functions with simulated network functions and served...
Exclusion Bias - Exclusion bias arises when relevant people, groups, or cases are left out of data collection, testing, or analysis in a way that distorts the resulting...
Existential risk - Existential risk is the possibility of an outcome that destroys humanity or permanently and drastically limits humanity's future potential.
Experimenter's bias - Experimenter's bias is a systematic influence on a study's procedures, observations, analysis, or interpretation arising from the researcher's expectations...
Expert system - An expert system is a computer program designed to apply specialized knowledge to problems within a defined domain.
Explainable AI - Explainable AI, often abbreviated XAI, is the study and practice of making AI systems and their outputs understandable to people through suitable...
Explainable empirical risk minimization - Explainable empirical risk minimization, or EERM, is a research approach that adds a user-related explainability objective to ordinary empirical risk...
Explainer - An explainer is a method, component, or tool that produces explanations of an AI system's behavior.
Explanation - An explanation is an account that helps someone understand an AI system, its output, or a relevant aspect of its behavior.
External validity - External validity is the extent to which findings from a study or evaluation apply beyond the particular conditions in which they were obtained.
Facial recognition - Facial recognition is the automated comparison of facial information to help verify or identify a person.
Fairness metric - A fairness metric is a quantitative measure used to examine whether an AI system meets a specified fairness criterion.
Fault tolerance - Fault tolerance is a system's ability to continue providing an acceptable service when one or more components fail.
Favorable label - A favorable label is the outcome category designated as desirable or beneficial for the people considered in a fairness analysis.
Feedback Loop Bias - Feedback Loop Bias occurs when a system's outputs influence the behavior or observations that later become its learning data, reinforcing a distorted pattern.
Fraud detection - Fraud detection is the process of identifying activity that may involve deception for an improper benefit, such as unauthorized transactions or false claims.
Funding Bias - Funding Bias is a distortion in research or evaluation that favors the interests or expectations of the organization or person providing financial support.
General data protection regulation - The General Data Protection Regulation, or GDPR, is the European Union's framework governing the processing of personal data and protecting individuals'...
Generative adversarial network - A generative adversarial network, or GAN, is a generative-learning framework in which a generator and a discriminator are trained with opposing objectives.
Group attribution bias - Group attribution bias is the tendency, in this glossary's machine-learning sense, to assume that a characteristic observed in one individual also applies...
Group fairness - Group fairness assesses how an AI system's decisions, errors, or benefits compare across defined groups of people.
Groupthink Bias - Groupthink Bias is a pattern in which pressure to agree with a group discourages independent criticism and contributes to poor decisions.
Hallucination - In AI, a hallucination is generated content that is false, unsupported or inconsistent with the information it is supposed to reflect, often presented...
Harmful bias - Harmful bias is a systematic pattern in an AI system or its surrounding process that contributes to unjustified disadvantage, exclusion, misrepresentation...
High-risk artificial intelligence system - A high-risk artificial intelligence system is an AI system placed in a category requiring stronger safeguards because of its intended use and potential...
Historical bias - Historical bias is bias already present in social conditions, institutions, or earlier decisions that becomes reflected in data used by an AI system.
Hostile Attribution Bias - Hostile Attribution Bias is the tendency to interpret another person's benign or ambiguous behavior as hostile.
Human-cognitive bias - Human-cognitive bias is a systematic tendency in how people notice, interpret, remember, or judge information that can influence decisions about AI systems...
Human-machine teaming - Human-machine teaming is an arrangement in which people and automated systems coordinate their contributions toward a shared task or objective.
Human system integration - Human system integration is the deliberate inclusion of people's capabilities, limitations, tasks, and working conditions in the design and operation of a...
Impact assessment - An impact assessment is a structured examination of how a proposed or operating system may affect people, organizations, society, or the environment.
Impersonation - Impersonation is presenting oneself, a message, or a system as another person or trusted entity.
Implicit bias - Implicit bias refers to automatic associations or assumptions that can influence how someone interprets information or makes a judgment without deliberate...
In-group bias - In-group bias is partiality toward people who belong to one's own group or share one's own characteristics.