ISO/IEC 22989: Artificial intelligence — concepts and terminology

ISO/IEC 22989:2022 establishes a common vocabulary for artificial intelligence (AI). It defines core concepts and terms for AI systems, data, lifecycle stages, roles, and AI properties, to enable consistent communication across stakeholders and to underpin other AI standards from ISO/IEC JTC 1/SC 42.

Scope and intent

  • Defines foundational AI concepts (e.g., AI system, model, algorithm, dataset; training/validation/testing data; lifecycle; deployment and operation)
  • Clarifies human roles and oversight (human-in-the-loop/on-the-loop/over-the-loop) and organizational responsibilities
  • Introduces terminology for key AI properties such as transparency, explainability, robustness, reliability, resilience, safety, security, privacy, and risk
  • Serves as a reference for related AI standards (risk management, trustworthiness, management systems, data quality, testing)

Position in the SC 42 family

  • Terminology baseline used by:
    • ISO/IEC 23894 — AI risk management
    • ISO/IEC 23053 — Framework for AI systems using machine learning
    • ISO/IEC 42001 — Artificial Intelligence Management System (AIMS)
    • ISO/IEC 24028 — Overview of trustworthiness in AI
    • ISO/IEC 5259 (series) — Data quality for analytics and ML
    • ISO/IEC/IEEE 29119-11 — Testing of AI-based systems

Quality Attributes Addressed (via terminology and concept coverage)

Attribute How it is addressed or framed in 22989
Reliability Terms for performance consistency and dependable behavior across lifecycle phases
Safety Concepts relating to harm, hazard, and safe operation of AI systems
Security Terminology linking security properties (confidentiality, integrity, availability) to AI contexts
Robustness Definitions around robustness to perturbations, uncertainty, and dataset shift
Resilience Concepts for recovery and continued operation under adverse conditions
Transparency Shared language for making AI system capabilities and limitations visible
Explainability Definitions for explainability/interpretability to support understanding of outputs
Accountability Roles and responsibilities, human oversight, assurance concepts
Fairness / Bias mitigation Terms for bias, fairness, and mitigation approaches
Privacy Concepts for data protection in AI lifecycles
Data quality Dataset, labeling, quality characteristics across training/validation/testing
Usability / human factors Human-in/on/over-the-loop, human oversight terminology
Maintainability Lifecycle and change-related terms that support maintainable operation
Traceability Terminology for artifacts, provenance, and evidence across the lifecycle

References

Official standard and committee

  • ISO/IEC 22989:2022 — Information technology — Artificial intelligence — Artificial intelligence concepts and terminology: https://www.iso.org/standard/74296.html
  • ISO Online Browsing Platform (sample/preview of terms): https://www.iso.org/obp/ui/#iso:std:iso-iec:22989:ed-1:v1:en
  • ISO/IEC JTC 1/SC 42 (Artificial intelligence) committee: https://www.iso.org/committee/6794475.html
  • ISO/IEC 23894:2023 — Artificial intelligence — Risk management: https://www.iso.org/standard/77304.html
  • ISO/IEC 23053:2022 — Framework for AI systems using machine learning: https://www.iso.org/standard/74296.html?browse=tc
  • ISO/IEC 42001:2023 — Artificial Intelligence Management System (AIMS): https://www.iso.org/standard/81230.html
  • ISO/IEC 24028:2020 — Artificial intelligence — Overview of trustworthiness: https://www.iso.org/standard/77608.html
  • ISO/IEC/IEEE 29119-11:2022 — Software testing — Part 11: Testing of AI-based systems: https://www.iso.org/standard/82074.html
  • ISO/IEC 5259 series — Data quality for analytics and ML: https://www.iso.org/committee/7943743/x/catalogue/p/0/u/1/w/0/d/0

National adoptions and authoritative summaries

  • BSI (UK): BS ISO/IEC 22989:2022 — Artificial intelligence — Concepts and terminology: https://shop.bsigroup.com/products/artificial-intelligence-concepts-and-terminology/standard
  • INCITS/ANSI (US) adoption: https://www.incits.org/standards/iso-iec-22989-2022
  • NIST AI RMF Crosswalks (alignment with ISO/IEC standards incl. 22989): https://www.nist.gov/itl/ai-risk-management-framework/ai-rmf-crosswalks