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
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

FrameworkClauseTitle
EU AI ActArticle 86Right to explanation of individual decision-making The right to explanation.
UK General Data Protection RegulationArticles 13 to 15Information and access Meaningful information.
UK General Data Protection RegulationArticles 22A to 22DAutomated 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.