Nondiscrimination - Nondiscrimination is the principle that people should not face unjustified disadvantage or exclusion on grounds that should not determine their treatment.
Offline learning - Offline learning is training a model from an already collected dataset rather than continually updating it as new operational examples arrive.
Opacity - Opacity is difficulty seeing or understanding how an AI system operates, reaches an output or affects a decision.
Operationalization - Operationalization is the process of translating a concept into observable measures or concrete procedures.
Opt-in - Opt-in is a choice mechanism in which a person actively agrees to participate in a specified activity or enable a specified use.
Opt-out - Opt-out is a mechanism through which a person declines, stops or excludes themselves from a specified activity or use.
Out-group homogeneity bias - Out-group homogeneity bias is the tendency to perceive members of a group outside one's own as more alike than members of one's own group.
Outcome - An outcome is a possible or observed result of a process, action or experiment. In probability, an outcome is one element of a defined sample space.
Personal data - Personal data is information relating to an identified or identifiable person.
Popularity Bias - Popularity bias is a tendency for already popular items to receive disproportionate exposure while less popular items receive little representation.
Population Bias - Population bias is a systematic mismatch between the people represented in a dataset and the population about which conclusions or predictions are intended.
Post-hoc explanation - A post-hoc explanation is an explanation produced by analyzing an already developed AI model or its behavior.
Post-processing - Post-processing is the adjustment or transformation of a model's output after inference.
Post-processing algorithm - A post-processing algorithm is a defined procedure that transforms a model's outputs after prediction.
Practical significance - Practical significance is the importance of an observed or estimated effect in its real-world context.
Pre-processing algorithm - A pre-processing algorithm is a defined procedure that prepares or transforms data before a model uses it.
Prediction bias - Prediction bias is the difference between the average of a model's predictions and the average of the corresponding observed labels in a dataset.
Predictive parity - Predictive parity is a group fairness criterion requiring equal precision, also called positive predictive value, across the groups being compared.
Preference alignment - Preference alignment is the process of adjusting an AI system's behavior to better match specified preferences about its outputs or actions.
Presentation Bias - Presentation bias is a distortion in behavior or resulting data caused by how information is displayed.
Privacy - Privacy concerns people's interests in how information about them and their lives is observed, collected, inferred, used and shared.
Privacy attack - A privacy attack is an attempt to learn protected information by exploiting a system's data, behavior or accessible outputs.
Privacy-by-design - Privacy by design is an approach that integrates privacy into the purpose, architecture, operation and lifecycle of a system from the outset.
Privacy-enhancing technology - A privacy-enhancing technology, or PET, is a technical method that helps reduce privacy risks by limiting data exposure, enabling protected computation or...
Privacy funnel - The privacy funnel is an information-theoretic approach to transforming data so that the released representation retains useful information while revealing...
Privacy leakage - Privacy leakage occurs when information released by a system reveals something about protected data beyond the intended disclosure.
Privileged protected attribute - A privileged protected attribute refers, in common fairness-toolkit terminology, to a value or set of values of a protected attribute designated as the...
Processing Bias - Processing bias, in the sense used by this source entry, concerns how the ease and effectiveness of processing information can influence feelings and...
Productization - Productization is the process of turning an AI capability, prototype, or research result into a usable and supportable product or service.
Profiling - Profiling, in the personal-data and AI context, is automated processing that evaluates or predicts aspects of an individual, such as preferences, behavior...
Prompt injection - Prompt injection is an attack that tries to make an AI application treat untrusted input as instructions that override or redirect its authorized task.
Protected attribute - A protected attribute is a personal characteristic associated with protection against discrimination under an applicable law or policy.
Protected class - A protected class is a category of people covered by particular protections against discrimination in a specified legal context.
Proxy (sensitive attributes) - A proxy for a sensitive attribute is another variable that serves as a stand-in for that attribute.
Pseudo-anonymization (pseudonymization) - Pseudonymization is processing that replaces or separates identifying information so that records cannot be attributed to a particular person without...
Ranking Bias - Ranking bias is a tendency to treat highly ranked results as especially relevant or important partly because of their position.
Rectification - Rectification, in personal-data governance, means correcting inaccurate personal information or completing information that is incomplete for its relevant...
Red teaming - Red teaming is a structured, authorized effort to find weaknesses, harmful behaviors, or exploitable failure modes in an AI system.
Reference class - A reference class is a group of cases used as the comparison basis for a judgment, estimate, or probability about a particular case.
Referential transparency - Referential transparency concerns whether an expression can be replaced by an equivalent expression or value without changing the meaning or relevant...
Regularized empirical risk minimization - Regularized empirical risk minimization, or RERM, is a model-training principle that balances average loss on training data with a penalty expressing a...
Relative risk or risk ratio - Relative risk, or risk ratio, compares the probability of a specified event in one group or condition with its probability in another.
Reliability - Reliability is the ability of a system to perform its required function without failure over a specified period and under stated conditions.
Reporting bias - Reporting bias is a systematic difference between what people record or describe and what actually occurs in the world.
Representation Bias - Representation bias occurs when the data used to develop or evaluate a system inadequately represent the population or conditions to which the system is...
Representational harm - Representational harm is harm arising from how an AI system depicts, describes, categorizes, or makes people and social groups visible.
Residual - A residual is the part of an observed value left unexplained by a fitted statistical model.