Control · C-097 · Transparency & Explainability
Explanations for affected individuals
People subject to an AI-supported decision receive a meaningful explanation of what was decided, the main reasons in their case and how to challenge it.
- Control type
- Directive
- Risks addressed
- 4
- Audience
- Heads-of · Practitioner
- Frameworks
- Article 86 · Articles 13 to 15 · Articles 22A to 22D
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-11
What it does
It gives the person affected what they need to understand and contest a decision. For each decision of consequence, the organisation can state in plain language the outcome, the principal factors that led to it for this person, the information about them that was used, and the route to have it looked at again. The explanation is offered with the decision or on request. The business owner of the decision is accountable; operations deliver it and compliance sets the standard. It is designed in and operates whenever such a decision is communicated.
How to implement it
- Decide which decisions require an explanation, starting with those the law covers.
- Design the system to record the main reasons for each decision at the time it is made.
- Write explanations for the reader: specific to the case, free of jargon, and honest about uncertainty.
- Tell the person what data was used and where it came from.
- Give front-line staff access to the reasons and training to explain them.
- Test explanations with members of the affected group.
Evidence it operates
Design: an explanation standard; reason capture in the decision record. Operation: sampled decision notices with case-specific reasons; logs of explanation requests and responses; staff training records; user testing of the wording.
Where it fits
At the point a decision is communicated, drawing on design choices made earlier. It is the primary control for inadequate explainability for affected individuals, and supports automated decision-making without safeguards and inadequate contestability and redress. It depends on explanation method validation for reasons that are true.
Limits
An explanation is only as faithful as the method that produced it. For complex models the main reasons are an approximation. Detailed reasons can help people game a system, which creates pressure to say less. A clear explanation of an unfair decision does not make it fair.
Risks this mitigates
Primary mappings
- Inadequate explainability for affected individuals High
- Delivers a meaningful explanation to the person affected. A person materially affected by an AI-supported decision is not given an explanation they can understand and use to challenge it.
Supporting mappings
- Automated decision-making without safeguards Critical
- Tells the person what was decided and why. AI is used to take significant decisions about individuals with no meaningful human involvement and without the safeguards UK data protection law requires.
- Inadequate contestability and redress High
- People cannot contest what they do not understand. People affected by an AI-supported decision cannot challenge it, cannot have it reviewed by someone with the authority and information to change it, or cannot obtain a remedy.
- Unfair algorithmic management High
- Tells staff how a decision was reached. AI is used in recruitment, task allocation, performance assessment or dismissal without fairness testing, explanation, human review or a route to challenge.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| EU AI Act | Article 86 | Right to explanation of individual decision-making The right to explanation. |
| UK General Data Protection Regulation | Articles 13 to 15 | Information and access Meaningful information. |
| UK General Data Protection Regulation | Articles 22A to 22D | Automated processing and significant decisions Information about the decision. |
Related terms
How to cite
Axiom Verity (2026). Explanations for affected individuals. Reference Library, C-097. https://axiom-verity.com/controls/explanations-for-affected-individuals. Reviewed 2026-11-11.
Found an error or a better source? Report a correction.