AI Governance Language Library

The set of policies, processes, controls, and oversight mechanisms that ensure AI systems operate within defined boundaries, produce accountable outcomes, and remain under meaningful human oversight.

The principle that every AI decision, outcome, and failure must be traceable to an identifiable responsible party within the organization.

The systematic process of providing confidence that AI systems operate as intended, within defined parameters, and in compliance with applicable requirements.

A structured examination of an AI system's governance controls, documentation, and outcomes to assess compliance with applicable policies and standards.

Systematic and unfair discrimination in AI system outputs resulting from flawed data, model design, or deployment context.

The state of conformance with applicable laws, regulations, standards, and internal policies governing AI system development and deployment.

A policy, procedure, or technical mechanism designed to manage AI governance risk and ensure AI systems operate within defined boundaries.

Documented, auditable records that demonstrate governance controls are in place, operating as designed, and producing intended outcomes.

The ability to describe, in understandable terms, how an AI system arrived at a particular decision or output.

The property of an AI system that produces outcomes free from unjustified discrimination across protected groups or characteristics.

A structured set of policies, standards, controls, and processes that define how an organization governs its AI systems.

The organizational structures, frameworks, tools, and processes that enable systematic AI governance at scale.

The full span of governance activities from AI system inventory and risk assessment through deployment, monitoring, and evidence collection.

A measure of the completeness, consistency, and operational effectiveness of an organization's AI governance program.

An organizational role responsible for the design, implementation, and oversight of an organization's AI governance program.

An event in which an AI system produces an outcome that violates governance policy, causes harm, or requires documented response.

A complete, documented record of all AI systems in use within an organization, including their purpose, risk classification, and governance status.

The risk of adverse outcomes resulting from errors in AI model design, data, assumptions, or deployment context.

The result produced by an AI system in response to an input — including decisions, recommendations, classifications, and generated content.

The organizational function of monitoring, reviewing, and controlling AI system behavior and outcomes.

A formal organizational statement that defines requirements, responsibilities, and standards for AI system development, deployment, and use.

The potential for adverse outcomes resulting from the development, deployment, or use of AI systems.

A structured evaluation of the governance risks associated with specific AI systems, considering their context, impact, and control environment.

The property of an AI system and its governance processes that makes them visible, understandable, and auditable to authorized parties.

The principle that organizations are responsible for the decisions and outcomes produced by algorithmic and AI systems they deploy.

Documented, auditable proof that governance controls are operating as designed and producing intended outcomes.

The meaningful authority of humans to review, intervene in, and override AI decisions — requiring defined roles, documented processes, and operational authority.

The policies, controls, and oversight mechanisms applied to AI and machine learning models throughout their development and deployment lifecycle.

Governance that functions within real operational environments — under production conditions, with real data, real decisions, and real consequences.

The systematic process of verifying that AI systems produce outcomes within defined, acceptable parameters — and that deviations are detected, documented, and addressed.

An approach to AI governance that is independent of specific AI vendors, platforms, or models — applicable regardless of the AI provider an organization uses.

An approach to AI development and deployment that prioritizes accountability, fairness, transparency, and human oversight.