Resilience - Resilience is the capacity of a system and its supporting organization to prepare for disruption, withstand or limit its effects, recover useful operation...
Responsible AI - Responsible AI is the practice of developing, deploying, using, and governing AI with attention to its effects on people, society, and the environment.
Retention limit - A retention limit is a defined boundary on how long data or records are kept for a particular purpose.
Retraining - Retraining updates a previously trained machine-learning model through another training process using different training data.
Risk-averse - Risk-averse describes a decision maker who prefers a certain outcome to an uncertain prospect with the same expected outcome value.
Risk control - A risk control is a measure intended to modify a risk, such as reducing the likelihood of an unwanted event, limiting its consequences, or improving...
Risk-neutral - Risk-neutral describes a decision maker who compares uncertain prospects by their expected value in a specified outcome measure, without an additional...
Risk-seeking - Risk-seeking describes a decision maker who prefers an uncertain prospect to a certain outcome with the same expected value.
Risk set - A risk set is the collection of study subjects or units that are being observed and are eligible to experience a specified event at a particular time.
Risk stratification - Risk stratification is the process of assigning cases to groups based on estimated risk of a specified outcome.
Risk tolerance - Risk tolerance is the amount and type of uncertainty or potential harm that an organization or person is prepared to accept while pursuing an objective.
Robot ethics - Robot ethics examines the moral questions raised by designing, building, deploying, and interacting with robots.
Robust AI - Robust AI is AI designed and evaluated to keep functioning acceptably when inputs, operating conditions, or other relevant circumstances vary.
Robustness - Robustness is the ability of a model or system to maintain acceptable behavior when specified conditions change or disturbances occur.
Safety - Safety is the condition and practice of preventing unacceptable harm from a system's operation, failures, or interactions with its surroundings.
Sampling bias - Sampling bias is systematic distortion arising when the process used to collect a sample gives an unrepresentative view of the population or task of interest.
Scalability - Scalability is a system's ability to handle growth in workload while meeting defined performance, quality, and resource requirements.
Security - Security is the protection of systems and information against unauthorized access, use, alteration, disruption, or other harmful interference.
Selection And Sampling Bias - Selection And Sampling Bias is the combined label used in the Artificial Intelligence Ontology for distortion introduced by a selection process that fails...
Selection bias - Selection bias is distortion in an inference caused by the process determining which observations are included in the analysis.
Selective Adherence Bias - Selective Adherence Bias is the tendency to accept algorithmic advice more readily when it agrees with pre-existing beliefs or stereotypes, while...
Self-healing system - A self-healing system detects certain faults and automatically takes predefined or policy-governed actions to restore acceptable operation.
Semantic mapping - Semantic mapping connects items according to their meaning, rather than relying only on matching words or formats.
Sensitive attribute - A sensitive attribute is a characteristic of a person that deserves particular care because of legal, ethical, social, or personal considerations.
Sensitivity analysis - Sensitivity analysis examines how a model's outputs or a decision's conclusions change when inputs, parameters, or assumptions are varied.
Shallow learning - Shallow learning is an informal term for machine-learning approaches that do not rely on a long hierarchy of learned transformations.
Sibyl attack - A Sybil attack occurs when one controlling entity presents multiple identities to gain influence that a system intends to distribute among independent...
Simulation component metadata - Simulation component metadata describes an individual component used in a simulation.
Simulation environment metadata - Simulation environment metadata describes the overall environment in which a simulation runs.
Simulation profile - A simulation profile, in the ITU machine-learning sandbox context, is a set of parameters and values describing the ML use case to be trained, evaluated or...
Societal Bias - Societal Bias refers to patterns of favoring or disfavoring people or groups based on social identities, demographic characteristics, or socially embedded...
Socio-technical system - A socio-technical system is a system whose behavior and outcomes arise from interacting social and technical elements.
Software testing - Software testing is a set of activities used to evaluate software and related work products, discover defects and provide evidence about whether...
Sparsity - Sparsity describes how much of a numerical representation consists of zero entries.
Straight-through processing - Straight-through processing, or STP, is the completion of a defined transaction or business process through connected automated steps without manual...
Streetlight Effect Bias - Streetlight effect bias is the tendency to direct investigation toward information that is easiest to obtain, even when other evidence would better answer...
Stress test - A stress test deliberately evaluates an AI model or system under demanding conditions to reveal weaknesses, limits, and failure behavior.
Structural risk minimization - Structural risk minimization (SRM) is a learning principle that balances fit to training data against the complexity or capacity of the candidate models.
Sunk Cost Fallacy Bias - Sunk cost fallacy bias is the tendency to let irrecoverable past expenditure justify continuing an activity, even when its remaining costs and benefits do...
Survivorship Bias - Survivorship bias occurs when analysis focuses on cases that remain visible after a selection process while overlooking cases that did not make it through.
Systemic bias - Systemic bias is a pattern of advantage or disadvantage arising from the procedures, norms, and practices of institutions or social systems.
Taxonomy - A taxonomy is an organized classification of categories, usually arranged from broader categories to more specific ones.
Technical control - A technical control is a safeguard implemented primarily through a system's hardware, software, or firmware.
Temporal Bias - Temporal bias is bias arising from differences in populations or behavior across time.
Traceability - Traceability is the ability to follow relevant connections between an AI system's inputs, data, processes, versions, outputs, and decisions through recorded...
Trained model - A trained model is the result of model training: a model whose adjustable state has been fitted using a training process.
Transparency - Transparency in AI is the availability of meaningful information about a system, its use, and its outputs to the people who need to understand or assess them.