A practical glossary of responsible machine learning principles, risks, evidence, controls, lifecycle decisions, and trustworthiness terms for product, engineering, risk, and business teams.
A practical glossary of responsible machine learning principles, risks, evidence, controls, lifecycle decisions, and trustworthiness terms for product, engineering, risk, and business teams.
By Rusaka Research · Published 2026-07-28 · Updated 2026-10-04 · 5858 words
Responsible machine learning, often shortened to responsible ML, is the disciplined design, development, evaluation, deployment, use, monitoring, and retirement of machine-learning systems so that their benefits and risks are understood and governed in context. It is not a single metric, a fairness library, or a final compliance review. It connects product purpose, data, models, people, operations, security, privacy, accessibility, accountability, and remedy throughout the system lifecycle.
This glossary gives business owners, product managers, data scientists, engineers, security and privacy teams, legal and compliance reviewers, risk owners, auditors, and affected stakeholders a shared vocabulary. Each term includes an operating meaning and a practical implication. Definitions are deliberately contextual: authoritative frameworks and laws can use the same word differently, and an organisation must confirm the meaning that applies to its sector, jurisdiction, role, and use case.
The glossary uses the NIST AI Risk Management Framework as a primary organising reference. NIST describes trustworthy AI characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. NIST also stresses that these characteristics interact, involve trade-offs, and cannot guarantee trustworthiness when treated separately. The OECD AI Principles and applicable legal requirements provide additional context for human-centred values, transparency, robustness, accountability, and lifecycle responsibility.
Responsible ML does not mean that every system must use the same control, threshold, explanation, or human-review design. Controls should match the intended purpose, affected people, plausible harms, uncertainty, reversibility, scale, and risk tolerance. A low-consequence internal forecast and a model that influences access to employment, credit, education, healthcare, insurance, or essential services require different evidence and oversight.
This resource is educational and does not establish legal compliance, certification, safety, fairness, or fitness for a particular purpose. Applicable law, contractual commitments, technical standards, organisational policies, sector rules, and qualified professional advice govern real decisions. A passed test is evidence about a defined condition, not proof that a system is responsible in every context.
Responsible-ML discussions often fail because teams use the same words for different things. Fairness may mean equal treatment, comparable error rates, removal of protected attributes, or a broader assessment of outcomes and rights. Explainability may refer to a global description of model behaviour, a local reason for one output, or a user-facing notice. Human oversight may mean approval before every action, review of exceptions, continuous supervision, or only the ability to stop the system. Without a shared definition, controls can appear complete while stakeholders expect different behaviour.
Technical vocabulary can also hide governance decisions. A model may meet an average accuracy threshold while failing a smaller group. A robust model may tolerate ordinary noise but remain vulnerable to deliberate manipulation. Removing a sensitive attribute may not remove its proxies. Monitoring input drift does not necessarily detect changed outcomes, new harms, or automation bias. Each measure needs a purpose, population, data source, threshold, decision rule, owner, limitation, and response.
Responsibility is sometimes assigned only to the model team. In practice, harms can arise from an inappropriate business objective, a poor workflow, inaccessible communication, weak data rights, unsafe integration, inadequate training, perverse incentives, downstream reuse, or lack of appeal. Responsible ML therefore treats the model as one component of a socio-technical system and assigns accountability across the lifecycle.
Frameworks can be misused as checklists or marketing claims. NIST describes AI risk management as continuous and contextual, and its Govern, Map, Measure, and Manage functions are not a certification. The presence of model cards, bias tests, or review boards does not prove that risks are acceptable. Evidence must connect to the real context of use and remain current after deployment.
Language changes as technology, standards, law, and practice evolve. NIST states that AI RMF 1.0 is being revised, and its trustworthy-AI glossary is released in beta. The glossary therefore records source boundaries, distinguishes definitions from local policy, and requires periodic review rather than presenting any term as permanently settled.
A common vocabulary makes cross-functional decisions testable. Product, engineering, legal, security, privacy, risk, operations, audit, and leadership can agree what evidence is required, who accepts residual risk, which threshold triggers intervention, and what affected people can expect.
Clear terms reduce false assurance. Teams can distinguish fairness testing from a conclusion about equitable outcomes, transparency from model disclosure, explanation from causal proof, validation from deployment monitoring, and human presence from effective human authority. These distinctions expose missing controls before a system affects people at scale.
Precise definitions also improve documentation and incident response. A system record can identify the intended purpose, context of use, affected parties, known limitations, test results, monitoring boundaries, escalation paths, and retirement conditions without forcing every reader to interpret technical shorthand.
The commercial value is better decision quality, not a guaranteed outcome. Teams can reject inappropriate use cases earlier, reduce avoidable rework, focus assurance on material risks, communicate limitations honestly, and preserve evidence for customers, partners, regulators, auditors, and internal governance.
A lifecycle practice for developing and using machine-learning systems with explicit attention to purpose, people, rights, risks, trustworthiness, evidence, accountability, and remedy. It includes product and organisational choices around the model, not only the training algorithm. The practical question is whether the complete system is appropriate and controlled for its defined context.
A machine-based system that produces outputs such as predictions, content, recommendations, or decisions from inputs for explicit or implicit objectives. Exact legal and policy definitions vary. Inventory the full service, interfaces, data flows, human decisions, dependencies, and downstream uses instead of treating a model file as the whole system.
A computational artefact whose behaviour is learned from data or interaction. A model may score, classify, rank, forecast, generate, retrieve, or control. Responsible operation also depends on preprocessing, thresholds, prompts, tools, business rules, interfaces, infrastructure, and human workflows.
An individual or organisation that plays an active role in the AI lifecycle, such as designing, developing, providing, deploying, operating, monitoring, procuring, evaluating, governing, or being accountable for a system. Name the actors and their decisions rather than assigning responsibility to AI as if it were an accountable person.
The connected stages from problem definition, design, data collection, development, validation, procurement, deployment, use, monitoring, change, incident response, and retirement. Decisions and evidence should persist across handovers because risks can be introduced or transformed at any stage.
The specific objective and authorised use for which the system is designed or supplied, including users, subjects, outputs, decisions, setting, and constraints. Write it narrowly enough to test. A vague purpose such as improve efficiency cannot define acceptable performance or prevent unsafe reuse.
The real environment in which the system operates: population, workflow, stakes, laws, infrastructure, incentives, decision authority, alternatives, and surrounding social conditions. Evidence from one context does not automatically transfer to another, even when the model is unchanged.
Use outside the intended design that can reasonably be anticipated from access, incentives, interfaces, prior incidents, or deployment conditions. Teams should test and constrain foreseeable misuse instead of treating all off-label behaviour as an unpredictable user fault.
A person, group, community, organisation, or environment that can experience benefit, burden, or harm from the system, including people who do not directly use it. Stakeholder mapping should include those subject to outputs, downstream decisions, surveillance, exclusion, or environmental effects.
The combination of uncertainty and possible consequences arising from an AI system. Risk can affect individuals, organisations, society, or the environment and can be positive or negative in some frameworks. Document sources, events, affected parties, likelihood or uncertainty, severity, controls, owner, and residual exposure.
A negative consequence such as physical injury, financial loss, discrimination, privacy intrusion, security compromise, loss of autonomy, exclusion, reputational damage, degraded service, or environmental impact. Harm analysis should consider severity, duration, scale, reversibility, distribution, and cumulative effects.
A context-dependent condition supported by characteristics such as validity, reliability, safety, security, resilience, accountability, transparency, explainability, interpretability, privacy, and managed harmful bias. NIST cautions that addressing characteristics individually does not ensure overall trustworthiness and that trade-offs require human judgement.
The extent to which a system measures, predicts, or performs what it is intended to in its stated context. A technically accurate model can be invalid for the business construct or decision it is used to represent. Validate the construct, label, outcome window, population, and decision connection.
The ability to perform consistently under expected conditions and over time. Reliability evidence can include repeated tests, error distributions, uptime, recovery, dependency behaviour, and stability across relevant environments. Average model accuracy alone does not establish system reliability.
The ability to maintain acceptable performance under variation, noise, perturbation, imperfect input, or stress within a defined boundary. Specify the disturbances tested and the failure threshold. Robustness to ordinary noise is different from adversarial robustness.
The ability to withstand, adapt to, and recover from adverse events, unexpected conditions, attacks, failures, or misuse. It includes detection, containment, fallback, restoration, learning, and continuity across the wider system, not simply model stability.
Freedom from unacceptable risk of physical, psychological, social, economic, or environmental harm within the context of use. Safety work connects hazards, scenarios, controls, human factors, testing, monitoring, incident response, and stop conditions. It is not synonymous with security.
Protection of system confidentiality, integrity, and availability, including models, data, prompts, tools, infrastructure, interfaces, and supply chains. Responsible ML security considers conventional threats and AI-specific abuse such as poisoning, extraction, evasion, prompt injection, unsafe tool use, and model or data leakage.
Appropriate governance of personal data and inferences across collection, training, evaluation, deployment, logging, access, retention, sharing, and deletion. De-identification claims need threat-aware evidence because models and joined datasets can create re-identification or inference risks.
A context-dependent assessment of whether processes and outcomes avoid unjustified advantage, disadvantage, or harm. Different fairness definitions can conflict. Select measures from the decision, affected groups, legal duties, error costs, baseline conditions, and stakeholder input rather than choosing the easiest metric.
Systematic tendencies that can create or reinforce harmful outcomes. Bias can enter through objectives, sampling, labels, measurement, features, model choice, thresholds, workflow, feedback, or social conditions. Not every statistical difference is harmful bias, and removing one attribute does not remove related structure.
A characteristic receiving legal, ethical, or policy protection or requiring special handling, such as disability, race, ethnicity, sex, age, religion, health, or biometric information. The applicable categories and permitted uses differ by jurisdiction and context. Governance should also consider intersectional groups.
A feature that stands in for, correlates with, or reveals another characteristic. Postcode, device, language, purchasing history, or network relationships may proxy sensitive status. Excluding a protected attribute can make disparities harder to measure while leaving proxies active.
A material difference in model or system outcomes across relevant groups, contexts, locations, devices, or conditions. Examine denominators, uncertainty, sample adequacy, selection effects, error costs, and operational impact before deciding whether a difference is acceptable or harmful.
The availability of appropriate information about the system, its purpose, operation, governance, limitations, data, decisions, and use. Meaningful transparency is audience-specific: an affected person, operator, auditor, customer, and regulator need different detail. Disclosure volume is not the same as understanding.
The extent to which information can communicate how or why a system produced behaviour or an output. Explanations may be global or local, technical or user-facing, and approximate or model-specific. Test whether the intended audience can use the explanation and state its limits.
The degree to which a person can understand the meaning or mechanism of a model, representation, input relationship, or output. Interpretability is relative to the audience and task. A simple model is not automatically valid, fair, safe, or transparent in operation.
The assignment of answerability and authority for system choices, outcomes, controls, incidents, and remedies. Name accountable people and governance bodies, define decision rights, preserve evidence, and ensure that responsibility cannot be delegated to a vendor, model, or review committee without authority.
The ability to follow the lineage of data, code, models, configurations, tests, approvals, outputs, and decisions through time. Traceability supports debugging, reproducibility, audit, incident response, and redress. It requires identifiers and linked records, not just document storage.
The practical ability of an authorised reviewer to examine the system, evidence, decisions, and controls against defined criteria. Access, logging, retention, reproducibility, documentation, independence, scope, and expertise determine whether a system is auditable.
A designed capability for people to understand relevant system behaviour, supervise use, intervene, override, stop, or challenge decisions with sufficient information, time, competence, authority, and protection from automation bias. A nominal reviewer who cannot act is not effective oversight.
A workflow in which a person participates in a defined step, often approval, labelling, exception review, or feedback. The phrase does not prove meaningful control. Measure reviewer workload, information quality, independence, disagreement, override behaviour, error detection, and escalation.
A tendency to favour an automated recommendation or overlook contrary evidence because the system appears authoritative or convenient. Interface design, time pressure, incentives, training, and weak feedback can amplify it. Effective oversight should test whether people can and do challenge the system.
The origin, collection conditions, permissions, transformations, lineage, quality decisions, and custody of data. Provenance should identify source and processing versions so teams can evaluate suitability, rights, contamination, leakage, bias, and downstream obligations.
The degree to which data covers the populations, conditions, behaviours, and edge cases relevant to the intended context. Representativeness is defined against a target use, not by dataset size. Historical data can faithfully represent an inequitable process while remaining unsuitable for the intended decision.
A change between the data distribution used for development or evaluation and the distribution encountered later. Input, label, concept, policy, population, or workflow change can affect validity. Detection requires reference windows, thresholds, impact analysis, and a response path.
A broad operational label for change in inputs, relationships, predictions, outcomes, or performance over time. Define which kind of drift is measured and why it matters. Drift without outcome evidence may be harmless, while harmful change can occur without a simple drift alert.
An input materially outside the conditions represented in development or validation. Systems should detect, reject, route, constrain, or safely handle such inputs where feasible. The boundary and fallback need testing; a confidence score is not always reliable evidence of familiarity.
Lack of complete knowledge about data, model parameters, predictions, future conditions, or consequences. Quantify uncertainty when methods support it, but also record structural unknowns and evidence gaps. Do not present a precise score as certainty when the context has changed.
Connected activities used to examine whether requirements, implementations, outputs, and real-world use meet defined criteria. TEVV should cover technical performance and socio-technical risks, use representative conditions, retain limitations, and continue after deployment.
Structured adversarial or exploratory testing intended to uncover failure, misuse, security weakness, harmful output, or control gaps. Scope the threat actors, goals, conditions, evidence, severity, and remediation. A single exercise does not prove safety or cover every deployment context.
A documented process for identifying purpose, affected parties, benefits, harms, rights, alternatives, data, controls, residual risks, accountability, consultation, and review before and during use. It should influence decisions rather than serve as a post-hoc form.
The types and levels of risk an accountable organisation is prepared to accept in pursuit of objectives. Translate it into measurable thresholds, prohibited uses, escalation rules, and authority. The organisation cannot declare away rights, legal duties, or unacceptable harm through internal tolerance.
Risk remaining after controls are applied. Record the evidence, uncertainty, affected parties, owner, approval, review date, and conditions that would reopen the decision. Passing a control test does not make residual risk zero.
Structured documentation describing a model, intended uses, evaluated conditions, performance, limitations, and relevant considerations. Model cards can improve communication, but their completeness, evidence, update process, and connection to the deployed system determine their value.
Structured documentation of a dataset or data asset, including motivation, composition, collection, processing, uses, access, maintenance, and limitations. It supports due diligence but does not substitute for inspecting provenance, rights, quality, representation, and context suitability.
An event in which an AI system causes, contributes to, or risks harm, loss, violation, control failure, or material service degradation. Define detection, containment, evidence preservation, notification, remedy, root-cause analysis, learning, and recurrence prevention.
Mechanisms that let affected people understand, challenge, correct, or seek remedy for relevant outcomes. Effective mechanisms are accessible, timely, independent enough for the context, connected to human authority, and capable of changing an outcome rather than merely collecting feedback.
The controlled retirement of a system, model, feature, dataset, or integration. Plan for replacement, continuity, retained decisions, legal records, model and data access, deletion, monitoring shutdown, downstream dependencies, communication, and lessons. A disabled endpoint does not complete retirement.
Describe the intended purpose, model and non-model components, users, subjects, outputs, decisions, locations, lifecycle stage, owners, vendors, integrations, and downstream uses. Identify which definitions come from applicable law, contracts, standards, policy, or local practice. Reject vague objectives and record non-AI alternatives.
Required output: system record, intended-purpose statement, context map, role map, authoritative-definition register, and excluded uses.
Map people and groups who use, are evaluated by, depend on, or can otherwise be affected by the system. Describe plausible benefit and harm scenarios, including unequal, cumulative, indirect, and environmental effects. Include accessibility, power, incentives, historical conditions, feedback loops, and the ability to avoid or challenge the system.
Required output: stakeholder map, impact assessment, harm taxonomy, consultation record, scenario set, and materiality decision.
Select relevant characteristics and convert them into testable requirements for validity, reliability, safety, security, resilience, accountability, transparency, explainability, interpretability, privacy, and harmful-bias management. Define populations, operating conditions, thresholds, uncertainty, prohibited failures, evidence owners, and trade-off decisions.
Required output: requirements matrix, metric dictionary, thresholds, trade-off log, risk tolerance, approval path, and stop conditions.
Document provenance, permissions, composition, transformations, labels, missingness, leakage, representation, sensitive attributes, proxies, train-evaluation separation, versioning, and quality. Link code, models, prompts, tools, environments, configurations, tests, and approvals to immutable identifiers. Review vendor evidence and limitations instead of relying on product claims.
Required output: data cards, model card, lineage, version record, access controls, evaluation plan, third-party evidence, and unresolved gaps.
Test under representative and adverse conditions. Examine error distributions, group and intersectional performance, calibration where relevant, robustness, security, privacy, explainability usefulness, accessibility, workflow effects, human oversight, automation bias, fallback, red-team findings, and recovery. Retain sample limitations and do not generalise beyond the tested context.
Required output: TEVV report, test datasets and versions, subgroup results, human-factors evidence, red-team record, limitations, defects, and release recommendation.
Have accountable people review the business need, alternatives, evidence, residual risk, affected-party safeguards, operational readiness, incident response, appeal, and retirement plan. Communicate system use and limitations to each audience at the level they need. Use staged rollout, access controls, fallback, and kill capability where consequence warrants them.
Required output: approval or rejection record, residual-risk acceptance, deployment plan, user and affected-party notices, training, fallback, and incident playbook.
Track inputs, outputs, outcomes, disparities, overrides, complaints, appeals, incidents, security signals, drift, dependency changes, and use outside purpose. Reconcile technical alerts with real outcomes and qualitative evidence. Define who reviews each signal, how quickly they respond, and which thresholds pause or retire the system.
Required output: monitoring plan, dashboards, evidence-quality checks, review cadence, alert ownership, escalation rules, and change triggers.
Contain incidents, preserve evidence, communicate appropriately, support correction and redress, analyse root causes across technology and workflow, and verify remediation. Reassess after material change. When value, validity, authority, safety, or support no longer justifies use, decommission the system and its dependencies under a controlled plan.
Required output: incident and remedy record, verified corrective actions, updated risk evidence, retirement decision, decommissioning evidence, and lessons for future systems.
An organisation proposes a model to prioritise incoming assistance applications for human review. The initial project brief says that the model will improve efficiency. The responsible-ML review narrows the intended purpose: the model may order a queue using specified application data, but it may not approve, deny, reduce, or close an application. Applicants, caseworkers, programme owners, auditors, and communities with unequal access to documentation are identified as affected parties and actors.
The team tests whether the target label is valid. Historic processing speed is rejected because it reflects staffing patterns and document availability rather than applicant need. The team defines error costs, protected and context-relevant groups, intersectional sample constraints, data provenance, missing-data handling, proxy risks, and a non-ML baseline. It measures overall and group performance with uncertainty instead of reporting one accuracy number.
Human-in-the-loop is converted from a label into a workflow design. Caseworkers receive relevant evidence and limitations, can change the priority, record a reason, and escalate suspected patterns. The team measures workload, review time, override rates, disagreement, and whether reviewers rubber-stamp the recommendation. Applicants receive a clear notice and an accessible way to correct information or challenge delay.
Deployment begins with a shadow evaluation and a bounded pilot. Monitoring covers queue time, outcome delay, group disparities, missing-data patterns, drift, overrides, complaints, security, and use outside the permitted purpose. A threshold for material disparity or a critical incident pauses automated prioritisation and returns the workflow to the documented manual process.
The evidence does not prove permanent fairness or compliance. It supports a time-bounded decision for the defined context. Material policy, population, data, workflow, model, or vendor change triggers reassessment. If the model no longer provides justified benefit or cannot remain within thresholds, the retirement plan removes the integration, preserves required records, and confirms that no downstream process still relies on the score.
Record what a term means for this system, who approved it, which source controls, and how the definition changes requirements. Avoid a glossary that never affects design or release decisions.
Evaluate objectives, data, models, interfaces, people, incentives, policies, infrastructure, and downstream use together. A technically improved model can still worsen a harmful workflow.
Report denominators, sample limits, intervals where appropriate, group definitions, intersectional gaps, error costs, and operational consequences alongside averages.
An explanation can describe inputs or behaviour; it does not prove that the objective, data, decision, or impact is appropriate. Review both mechanism and legitimacy.
Give people information, time, skill, independence, authority, fallback, and escalation. Test actual override and error-detection behaviour under realistic workload.
Link data, code, model, configuration, prompts, tools, evaluations, approvals, notices, and monitoring to the deployed version and context.
Drift signals can help diagnose change but cannot replace outcome, disparity, complaint, override, incident, and misuse evidence.
Affected people need accessible channels, clear ownership, correction authority, timelines, and records. A feedback inbox without the ability to change an outcome is not redress.
Privacy, interpretability, accuracy, latency, security, access, and fairness can interact. Document options, affected parties, evidence, authority, and residual risk instead of claiming every characteristic was maximised.
Frameworks, definitions, and laws change. Record version and access date, monitor revisions, and distinguish voluntary guidance from binding requirements.
Principles guide decisions but need owners, requirements, measures, thresholds, evidence, escalation, and consequences to change system behaviour.
Other variables can encode the same structure, and attributes may be needed to measure disparities lawfully. Use qualified legal and technical review.
Definitions can conflict and may ignore base rates, uncertainty, intersectionality, error costs, workflow, or historic disadvantage. Choose evidence from context and impact.
Reviewers can lack time, information, authority, independence, or confidence to challenge automation. Test the workflow and human factors.
Documentation becomes stale after changes in data, model, integration, population, policy, or use. Version it and connect updates to release governance.
Many explanation methods approximate associations or local behaviour. State method assumptions, stability, fidelity, audience, and decision limitations.
Average performance can hide severe errors, group disparities, poor calibration, rare hazards, or inappropriate objectives. Test decision-relevant conditions.
Input drift can miss changed outcomes, harmful feedback loops, security incidents, automation bias, and off-purpose use. Monitor the full system.
Third-party evidence and contracts matter, but the deploying organisation still needs ownership for its context, integration, people, decisions, monitoring, and remedy.
Using the NIST AI RMF or another framework does not itself certify trustworthiness, legal compliance, safety, or fairness. Report the exact evidence and limits.
For every local term, retain the preferred label, definition, source, source version, jurisdiction or scope, related terms, practical implication, owner, approval date, review trigger, and examples. Mark whether the definition is legal, standards-based, policy, technical, or operational. Do not silently rewrite historical evidence when a definition changes.
Link glossary changes to requirements, templates, controls, training, product documentation, and live system records. A term is operational only when affected teams can retrieve it at the point of decision and understand which definition controls.
Start from the decision and harm, not a metric catalogue. Identify who can be affected, what errors or burdens mean for them, the lawful and meaningful comparison groups, historic and selection effects, available ground truth, sample adequacy, and whether the intervention changes opportunity, treatment, outcome, or access.
Test multiple relevant views and uncertainty. Document incompatibilities and the reason for the selected threshold. Review qualitative evidence, process fairness, accessibility, contestability, and operational consequences alongside statistical results.
Define what the person observes, understands, decides, changes, records, escalates, and stops. Test realistic workload, time pressure, interface design, missing context, incentives, training, system confidence, and disagreement. A reviewer should know when the system is outside its intended boundary.
Track override quality rather than aiming for a particular override rate. Very low rates can signal either strong model performance or automation bias; high rates can signal poor model fit, unclear policy, or expert correction. Investigate with outcome and workflow evidence.
Responsible ML is a continuous, context-specific practice for governing a complete machine-learning system. It starts with whether the purpose is appropriate, identifies actors and affected parties, translates trustworthiness into requirements, tests the system and workflow, assigns residual risk to accountable people, monitors real use and outcomes, supports remedy, and retires systems that no longer remain justified.
Use this glossary to make decisions precise, not to manufacture consensus. Record authoritative and local meanings, connect every material term to evidence and authority, and disclose uncertainty. Framework adoption, documentation, or a passed metric can support a decision, but none guarantees that a system is responsible, trustworthy, fair, safe, or legally compliant.
Responsible ML is the lifecycle practice of designing, developing, evaluating, deploying, using, monitoring, and retiring machine-learning systems with explicit governance of purpose, affected people, risks, trustworthiness, evidence, accountability, and remedy.
They overlap. Responsible ML focuses on systems that learn from data or interaction, while responsible AI can also cover rule-based, generative, autonomous, and other AI approaches. The exact boundary depends on the governing framework and system.
NIST identifies characteristics including validity and reliability, safety, security and resilience, accountability and transparency, explainability and interpretability, privacy enhancement, and fairness with harmful bias managed. Their relevance and trade-offs must be assessed in context; no checklist guarantees trustworthiness.
No. Other variables can act as proxies, historic labels and objectives can encode disparities, and removing attributes can prevent measurement. Use context-specific legal, technical, and stakeholder review.
Explainability focuses on communicating how or why behaviour or an output arose. Transparency is broader and covers appropriate information about purpose, governance, data, operation, limitations, decisions, and use. Both are audience- and context-dependent.
Not by itself. The reviewer needs adequate information, time, competence, independence, authority, fallback, and escalation. Teams should measure overrides, disagreement, error detection, workload, and automation bias.
Choose measures from the decision, affected groups, legal duties, historic context, error costs, sample adequacy, uncertainty, and stakeholder evidence. Different mathematical definitions can conflict, so record the rationale, trade-offs, and decision rule.
Monitor inputs, performance, calibration where relevant, outcomes, disparities, uncertainty, overrides, complaints, appeals, incidents, security, privacy, drift, dependencies, and use outside purpose. Assign thresholds, owners, and responses.
A model card is structured documentation of a model, intended uses, evaluated conditions, performance, limitations, and relevant considerations. It supports communication but needs current evidence and connection to the deployed system.
NIST describes AI RMF 1.0 as voluntary. Other laws, contracts, standards, sector rules, and organisational policies may be mandatory. Confirm the requirements that apply to each role, system, and jurisdiction.
Reassess after material changes in purpose, population, data, labels, model, threshold, workflow, vendor, dependency, law, policy, risk, incidents, or outcomes, and at the scheduled cadence set by its risk.
No. It is educational guidance and a shared vocabulary. Compliance and responsible operation require context-specific requirements, evidence, decisions, monitoring, and qualified legal, technical, security, privacy, safety, and domain review.