EU AI Act Art. 86: right to explanation of individual decision-making
A person subject to a deployer's decision based on an Annex III system (except point 2) with legal or similarly significant adverse effects may obtain clear and meaningful explanations of the system's role and the main elements of the decision, where Union law does not already give the right
AIGE-OBL-EUAIA-ART86. Drawn from chapter 08.
Text alternative
- Clause: EU AI Act, Art. 86.
- Duty holder: Deployer.
- Applies from: 2027-12-02, Deferred.
- Artefact: Explanation record per decision.
- Layers: Layer 02, Layer 04, Layer 05.
- Evidence record: Evidence record v1.
- Record schema: Evidence record.
- The same topic in 22 other frameworks; the crosswalk section below links each clause.
Data table: the dates
| Date | Step | Detail |
|---|---|---|
| 2027-12-02 | Deferred | Annex III; Chapter IX applies from 2026-08-02, but the right needs a classified Annex III system (reading, to be confirmed) |
| 2030-08-02 | Later step | Deadline for legacy high-risk systems intended for use by public authorities (Art. 111(2)) |
- Id
AIGE-OBL-EUAIA-ART86- Instrument
- EU AI Act (post-Omnibus) law
- Compared side by side
- ISO 42001 vs EU AI Act · NIST AI RMF vs EU AI Act
- Clause
- Art. 86
- Duty holder
- Deployer
- Authority
- National MSA
- Applies from
- Deferred · Annex III; Chapter IX applies from 2026-08-02, but the right needs a classified Annex III system (reading, to be confirmed)
- Later dates
-
- Deadline for legacy high-risk systems intended for use by public authorities (Art. 111(2))
- System class
- High-risk (Annex III)
The artefact that evidences it
Explanation record per decision (system and model version, reason codes, determinative or advisory role, human decider); request-handling workflow and response log.
Data table: every relation
| Family | Item | Relation |
|---|---|---|
| Patterns | Decision Notice & Contest Path | Direct |
| Patterns | Explanation Artefact | Direct |
| Cases | SyRI: a fraud risk model no court could verify | Cites the article |
| Same crosswalk topic | AI Verify 1.1.6, 1.7.2, 1.1.1, 2.2.1, 1.1.4, 9.4.1, 5.14.1, 6.5.1, 4.6.1 | Core clause |
| Same crosswalk topic | CSA AICM MDS-03, MDS-04, GRC-13, GRC-14, AIS-06, CCC-01, DSP-02 | Core clause |
| Same crosswalk topic | China AI Labelling Label Art. 4, Label Art. 5 | Core clause |
| Same crosswalk topic | China Deep Synthesis DeepSyn Art. 16, DeepSyn Art. 17 | Core clause |
| Same crosswalk topic | China GenAI Measures GenAI Art. 12, GenAI Art. 19 | Core clause |
| Same crosswalk topic | CoE Convention CoE Art. 14(2), CoE Art. 15(2), CoE Art. 16(2)(g) | Core clause |
| Same crosswalk topic | EU AI Act Art. 11, Art. 13, Art. 53, Art. 50, Art. 18, Art. 43, Art. 53(1)(d), Art. 50(2), 50(4), Art. 13(3)(b)(iv)–(v), Art. 26, Art. 25, Art. 43(4), Art. 20, Art. 79 | Core clause |
| Same crosswalk topic | G7 Code G7 Action 3 | Core clause |
| Same crosswalk topic | GAO AI Accountability 1.9, 3.5, 1.7, 4.4, 4.5 | Core clause |
| Same crosswalk topic | GDPR Arts. 13–14, Art. 30, Art. 15(1)(h), Art. 13(2)(f), Art. 22(3) | Core clause |
| Same crosswalk topic | GPAI Code Transparency 1.1, Transparency 1.2 | Core clause |
| Same crosswalk topic | ISO 42001 7.5, A.6, A.8, A.8.2, A.6.2.5, A.6.2.6, A.9 | Core clause |
| Same crosswalk topic | Korea AI Act Art. 31, Art. 34(1)(2) | Core clause |
| Same crosswalk topic | NIST AI RMF MAP 1, MEASURE 2.8, MAP 1.6, MEASURE 2.9, MANAGE 2.4, MANAGE 4.1, GOVERN 1.7 | Core clause |
| Same crosswalk topic | OECD AI Principles OECD 1.3, OECD 1.4 | Core clause |
| Same crosswalk topic | Singapore GenAI GenAI 3 | Core clause |
| Same crosswalk topic | UK ATRS ATRS Tier 1, ATRS 2.2, ATRS 2.3.5 | Core clause |
| Same crosswalk topic | UK DUAA UK GDPR Art. 22C | Core clause |
| Same crosswalk topic | China Algo. Rec. AlgoRec Art. 16, AlgoRec Art. 17 | Related clause |
| Same crosswalk topic | GB/T 45654 GB/T 45654 Content labelling | Related clause |
| Same crosswalk topic | Singapore Agentic Agentic 2.3.3 | Related clause |
| Same crosswalk topic | TC260 Framework 3.0 TC260 5.3, TC260 5.3.19 | Related clause |
Patterns that build it
- Explanation Artefact (layer 4 and 5)
- Decision Notice & Contest Path (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.
Documentation and transparency
- EU AI Act Art. 11 Technical documentation (core)
- EU AI Act Art. 13 Transparency and provision of information to deployers (core)
- EU AI Act Art. 53 Obligations for providers of general-purpose AI models (core)
- ISO 42001 7.5 Documented information (core)
- ISO 42001 A.6 AI system life cycle (core)
- ISO 42001 A.8 Information for interested parties (core)
- China AI Labelling Label Art. 4 Explicit labels for generated content (core)
- China AI Labelling Label Art. 5 Implicit (metadata) labels (core)
- China GenAI Measures GenAI Art. 12 Labelling of generated content (core)
- China Deep Synthesis DeepSyn Art. 16 Implicit technical labels (core)
- China Deep Synthesis DeepSyn Art. 17 Conspicuous labels for confusable content (core)
- GPAI Code Transparency 1.1 Drawing up and keeping up-to-date model documentation (core)
- GDPR Arts. 13–14 Information to be provided to the data subject (core)
- CSA AICM MDS-03 Model Documentation (core)
- Korea AI Act Art. 31 Transparency: prior notice, output labelling, realistic synthetic content (core)
- UK ATRS ATRS Tier 1 Summary information (core)
- Singapore GenAI GenAI 3 Trusted Development and Deployment (core)
- CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities (core)
- OECD AI Principles OECD 1.3 Transparency and explainability (core)
- G7 Code G7 Action 3 Publicly report capabilities, limitations and domains of use (core)
- GAO AI Accountability 1.9 Transparency: enable external stakeholders to access information on the design, operation, and limitations of the AI system (core)
- GAO AI Accountability 3.5 Documentation: document the methods for assessment, performance metrics, and outcomes of the AI system (core)
- AI Verify 1.1.6 Transparency: publicly report accuracy, intended use cases, limitations, risk assessment and mitigations to stakeholders (core)
- AI Verify 1.7.2 Transparency: make model or system cards publicly available where warranted by public interest (core)
- EU AI Act Art. 50 Transparency obligations for providers and deployers of certain AI systems
- NIST AI RMF MAP 1 MAP 1: Context is established and understood
- NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability are examined and documented
- China GenAI Measures GenAI Art. 19 Disclosure to regulators
- China Algo. Rec. AlgoRec Art. 16 Notice that recommendation is used
- GB/T 45654 GB/T 45654 Content labelling Generated-content labelling requirements (clause not verified)
- EU AI Act Art. 18 Documentation keeping
- EU AI Act Art. 43 Conformity assessment
- EU AI Act Art. 53(1)(d) Public summary of the content used for training
- EU AI Act Art. 50(2), 50(4) Machine-readable marking of synthetic content; disclosure of deep fakes
- GPAI Code Transparency 1.2 Providing relevant information
- GDPR Art. 30 Records of processing activities
- NIST AI RMF MAP 1.6 MAP 1.6: System requirements are elicited from and understood by relevant AI actors. Design decisions take socio-technical implications into account to address AI risks
- NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance
- CSA AICM MDS-04 Model Documentation Requirements
- Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview
- UK ATRS ATRS 2.2 Description and rationale
- CoE Convention CoE Art. 15(2) Notification of interaction with an AI system
- GAO AI Accountability 1.7 Specifications: establish and document technical specifications
- AI Verify 1.1.1 Transparency: design an in-house policy on communication to consumers that articulates the principles for transparency
Explainability and right to explanation
- EU AI Act Art. 13(3)(b)(iv)–(v) Information relevant to explain output; performance for specific persons or groups (core)
- GDPR Art. 15(1)(h) Meaningful information about the logic involved (core)
- NIST AI RMF MEASURE 2.9 MEASURE 2.9: The AI model is explained, validated, and documented, and AI system output is interpreted within its context as identified in the MAP function to inform responsible use and governance (core)
- CSA AICM GRC-13 Explainability Requirement (core)
- CSA AICM GRC-14 Explainability Evaluation (core)
- Korea AI Act Art. 34(1)(2) Explanation plan: result, main criteria, training-data overview (core)
- UK DUAA UK GDPR Art. 22C Safeguards for automated decision-making (core)
- OECD AI Principles OECD 1.3 Transparency and explainability (core)
- AI Verify 2.2.1 Explainability: document the rationale and trade-offs of a less explainable model and apply explainable AI techniques (core)
- AI Verify 1.1.4 Transparency: tell people using or affected by the system the factors behind a decision and that it was made wholly or partly by an automated system (core)
- GDPR Art. 13(2)(f) Existence of automated decision-making
- GDPR Art. 22(3) Right to obtain human intervention and to contest the decision
- ISO 42001 A.8.2 System documentation and information for users (clause not verified)
- NIST AI RMF MEASURE 2.8 MEASURE 2.8: Risks associated with transparency and accountability as identified in the MAP function are examined and documented
- UK ATRS ATRS 2.3.5 Appeals and review
- Singapore GenAI GenAI 3 Trusted Development and Deployment
- CoE Convention CoE Art. 14(2) Documentation sufficient to contest decisions; complaint to authorities
- China Algo. Rec. AlgoRec Art. 17 Explain where an algorithm significantly affects user rights
Deployment, change and decommissioning
- EU AI Act Art. 26 Obligations of deployers of high-risk AI systems (core)
- ISO 42001 A.6.2.5 AI system deployment (core) (clause not verified)
- ISO 42001 A.6.2.6 AI system operation and monitoring (core) (clause not verified)
- NIST AI RMF MANAGE 2.4 MANAGE 2.4: Mechanisms are in place and applied, and responsibilities are assigned and understood, to supersede, disengage, or deactivate AI systems that demonstrate performance or outcomes inconsistent with intended use (core)
- NIST AI RMF MANAGE 4.1 MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including mechanisms for capturing and evaluating input from users and other relevant AI actors, appeal and override, decommissioning, incident response, recovery, and change management (core)
- NIST AI RMF GOVERN 1.7 GOVERN 1.7: Processes and procedures are in place for decommissioning and phasing out AI systems safely and in a manner that does not increase risks or decrease the organization's trustworthiness (core)
- CSA AICM AIS-06 Secure Application Deployment (core)
- AI Verify 9.4.1 Accountability: a policy to deactivate, decommission and phase out AI systems safely (core)
- AI Verify 5.14.1 Security: proper and secure disposal of data and models (core)
- AI Verify 6.5.1 Robustness: review changes in code, input data and output data to decide when a new robustness review is due (core)
- EU AI Act Art. 25 Responsibilities along the AI value chain
- EU AI Act Art. 43(4) New conformity assessment on substantial modification
- EU AI Act Art. 20 Corrective actions and duty of information
- EU AI Act Art. 79 Procedure at national level for dealing with AI systems presenting a risk
- ISO 42001 A.9 Use of AI systems
- CSA AICM CCC-01 Change Management Policy and Procedures
- CSA AICM DSP-02 Secure Disposal
- Singapore Agentic Agentic 2.3.3 When deploying, continuously monitor and test
- CoE Convention CoE Art. 16(2)(g) Testing before first use and when significantly modified
- OECD AI Principles OECD 1.4 Robustness, security and safety
- TC260 Framework 3.0 TC260 5.3 Operators' safety guidelines
- TC260 Framework 3.0 TC260 5.3.19 Re-assessment on material change
- GAO AI Accountability 4.4 Ongoing assessment: assess the utility of the AI system to ensure its relevance to the current context
- GAO AI Accountability 4.5 Scaling: identify conditions, if any, under which the AI system may be scaled or expanded beyond its current use
- AI Verify 4.6.1 Safety: deployment strategies such as blue-green and canary deployments
Cases that cite this article
Source
Chapter 08, section EU AI Act, post-Omnibus, checked against its sources on the review date above.
Machine-readable
- This obligation:
/api/v1/obligations/aige-obl-euaia-art86.json - The register:
/api/v1/obligations.json· CSV - Schema and stability promise: open data and API
Cite this obligation
García Aibar, J. (2026). EU AI Act Art. 86: right to explanation of individual decision-making (AIGE-OBL-EUAIA-ART86). 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-euaia-art86. CC BY 4.0
BibTeX
@misc{aige2026obligation,
author = {Jorge García Aibar},
title = {{EU AI Act Art. 86: right to explanation of individual decision-making (AIGE-OBL-EUAIA-ART86)}},
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-euaia-art86},
note = {Version 0.5.0}
}