ISO 42001 A.5: Assessing impacts of AI systems

Impact assessment process

By Jorge García Aibar Reviewed 2026-09-19

From clause to evidence Build the artefact, then file every output it produces as a record that names AIGE-OBL-ISO42001-A5. Drawn from chapter 08.

Text alternative

Clause: ISO 42001, A.5.
Duty holder: Not stated.
Applies from: No date, Voluntary.
Artefact: Impact assessment as code.
Layers: Layer 01, Layer 03.
Evidence record: Impact assessment, +1 more.
Record schemas: Impact assessment , Use-case record .
The same topic in 15 other frameworks; the crosswalk section below links each clause.

Id: AIGE-OBL-ISO42001-A5
Instrument: ISO/IEC 42001 standard
Compared side by side: ISO 42001 vs EU AI Act · NIST AI RMF vs ISO 42001
Clause: A.5
Applies from: No date Voluntary · Voluntary management-system standard (2023); no presumption of conformity

The artefact that evidences it

Impact assessment as code; FRIA/DPIA linkage (ISO/IEC 42005).

What this obligation connects to Solid edges are direct relations, dashed ones looser (a case that cites the same article, a framework whose clauses only touch the topic); patterns and controls take the colour of their stack layer. Select a node to open it; the lists below give every relation in full.

Data table: every relation

Relations of AIGE-OBL-ISO42001-A5
Family	Item	Relation
Patterns	Drift & Fairness Monitor	Direct
Patterns	Fairness Eval Suite	Direct
Patterns	Use-Case Intake & Risk Tiering	Direct
Same crosswalk topic	AI Verify 11.2.1, 4.2.1, 7.1.1, 7.2.1, 7.8.1	Core clause
Same crosswalk topic	CSA AICM GRC-10, DSP-09, GRC-11	Core clause
Same crosswalk topic	China GenAI Measures GenAI Art. 17, GenAI Art. 4(2)	Core clause
Same crosswalk topic	CoE Convention CoE Art. 16, CoE Art. 10	Core clause
Same crosswalk topic	EU AI Act Art. 27, Art. 9, Art. 10(2)(f)–(g), Art. 4a	Core clause
Same crosswalk topic	GAO AI Accountability 1.5, 2.7, 3.8	Core clause
Same crosswalk topic	GDPR Art. 35, Art. 36, Art. 5(1)(a), Art. 9	Core clause
Same crosswalk topic	ISO 42001 6.1.4, 8.4	Core clause
Same crosswalk topic	ISO 42005 42005 5.8, 42005 6.8, 42005 5.12	Core clause
Same crosswalk topic	Korea AI Act Art. 35	Core clause
Same crosswalk topic	NIST AI RMF MAP 3, MAP 5, MEASURE 2.11, GOVERN 3.1	Core clause
Same crosswalk topic	TC260 Framework 3.0 TC260 Appendix 1, TC260 2.2	Core clause
Same crosswalk topic	UK ATRS ATRS 2.5.1, ATRS 2.4.2	Core clause
Same crosswalk topic	China Algo. Rec. AlgoRec Art. 21	Related clause
Same crosswalk topic	OECD AI Principles OECD 1.2	Related clause

Patterns that build it

Use-Case Intake & Risk Tiering (layer 1 and 2)
Fairness Eval Suite (layer 3)
Drift & Fairness Monitor (layer 4 and 5)

The same topic in other frameworks

From the topic crosswalk: the clauses filed under the same topics as this one. Mappings are illustrative, not a claim of conformity.

Impact assessment

EU AI Act Art. 27 Fundamental rights impact assessment for high-risk AI systems (core)
ISO 42001 6.1.4 AI system impact assessment (core)
ISO 42001 8.4 AI system impact assessment (operation) (core)
NIST AI RMF MAP 3 MAP 3: AI capabilities, targeted usage, goals, and expected benefits and costs are understood (core)
NIST AI RMF MAP 5 MAP 5: Impacts to individuals, groups, communities, organizations, and society are characterized (core)
TC260 Framework 3.0 TC260 Appendix 1 Grading principles (core)
China GenAI Measures GenAI Art. 17 Security assessment (core)
GDPR Art. 35 Data protection impact assessment (core)
ISO 42005 42005 5.8 Performing the AI system impact assessment (core) (clause not verified)
ISO 42005 42005 6.8 Actual and reasonably foreseeable impacts (core) (clause not verified)
CSA AICM GRC-10 AI Impact Assessment (core)
Korea AI Act Art. 35 Impact assessment (best-effort duty) (core)
UK ATRS ATRS 2.5.1 Impact assessments (core)
AI Verify 11.2.1 Inclusive growth, societal and environmental well-being: assess the impact of the AI system on individuals and society (core)
EU AI Act Art. 9 Risk management system
TC260 Framework 3.0 TC260 2.2 Safety risks in the application of AI
GDPR Art. 36 Prior consultation
ISO 42005 42005 5.12 Monitoring and review (clause not verified)
CSA AICM DSP-09 Data Protection Impact Assessment
CoE Convention CoE Art. 16 Risk and impact management framework
GAO AI Accountability 1.5 Stakeholder involvement: include diverse perspectives from a community of stakeholders throughout the AI life cycle
AI Verify 4.2.1 Safety: carry out an assessment of materiality on key stakeholders

Fairness and non-discrimination

EU AI Act Art. 10(2)(f)–(g) Examination for possible biases; measures to detect, prevent and mitigate them (core)
EU AI Act Art. 4a Special-category data for bias detection (core)
GDPR Art. 5(1)(a) Lawfulness, fairness and transparency (core)
NIST AI RMF MEASURE 2.11 MEASURE 2.11: Fairness and bias as identified in the MAP function are evaluated and results are documented (core)
CSA AICM GRC-11 Bias and Fairness Assessment (core)
CoE Convention CoE Art. 10 Equality and non-discrimination (core)
China GenAI Measures GenAI Art. 4(2) Prevent discrimination in design, data, training and service (core)
GAO AI Accountability 2.7 Bias: assess reliability, quality, and representativeness of the data used in operation, including potential biases (core)
GAO AI Accountability 3.8 Bias: identify potential biases, inequities, and other societal concerns resulting from the AI system (core)
AI Verify 7.1.1 Fairness (technical test): measure algorithmic fairness of predictions against ground truth, with metrics chosen for the sensitive feature (core)
AI Verify 7.2.1 Fairness (technical test): benchmark and red-team for harms against individuals or groups (generative AI) (core)
GDPR Art. 9 Processing of special categories of personal data
NIST AI RMF GOVERN 3.1 GOVERN 3.1: Decision-making related to mapping, measuring, and managing AI risks throughout the lifecycle is informed by a diverse team
UK ATRS ATRS 2.4.2 Model specification
OECD AI Principles OECD 1.2 Rule of law, human rights and democratic values, including fairness and privacy
China Algo. Rec. AlgoRec Art. 21 No unreasonable differential treatment in trading conditions
AI Verify 7.8.1 Fairness: check that training data is representative of the end-user population, resampling or collecting more data where needed

Source

Chapter 08, section ISO/IEC 42001, 42005 and 42006, checked against its sources on the review date above.

Machine-readable

This obligation: /api/v1/obligations/aige-obl-iso42001-a5.json
The register: /api/v1/obligations.json · CSV
Schema and stability promise: open data and API

Cite this obligation

García Aibar, J. (2026). ISO 42001 A.5: Assessing impacts of AI systems (AIGE-OBL-ISO42001-A5). In AI Governance Engineering: The Thesis & Body of Knowledge (v0.5.0). https://doi.org/10.5281/zenodo.22956197. https://aigovernanceengineer.com/obligations/aige-obl-iso42001-a5. CC BY 4.0

BibTeX

@misc{aige2026obligation,
  author       = {Jorge García Aibar},
  title        = {{ISO 42001 A.5: Assessing impacts of AI systems (AIGE-OBL-ISO42001-A5)}},
  howpublished = {In AI Governance Engineering: The Thesis \& Body of Knowledge},
  year         = {2026},
  version      = {0.5.0},
  doi          = {10.5281/zenodo.22956197},
  url          = {https://aigovernanceengineer.com/obligations/aige-obl-iso42001-a5},
  note         = {Version 0.5.0}
}