Risk · R-112 · Fairness, Bias & Impact
Mis-specified fairness measurement
Fairness is tested with a metric, threshold or grouping that does not match the legal test or the harm, so a system passes while remaining unfair.
- Severity
- Medium
- Audience
- Heads-of · Practitioner
- Frameworks
- Section 19 · Article 10 · MEASURE 1
- Responsible AI domain
- Lifecycle stage
- Reviewed
- 2026-11-11
What it is
There is no single measure of fairness. Equal approval rates, equal error rates and equal accuracy of scores are different things, and in general cannot all hold at once. Choosing among them is a judgement about which harm matters in this use. The risk is that the choice is made by default, by a tool's settings or by what is easiest to compute. Related errors are thresholds borrowed from another jurisdiction, groups defined too broadly, and failure to look at combinations of characteristics, where a system fair to women and to older people may still be unfair to older women.
How it happens
- A fairness library's default metric is used without discussion.
- A rule of thumb from another legal system is applied as a pass mark.
- Groups are merged to obtain large enough samples.
- Intersections are not examined.
Why it matters
A passed test closes the question inside the organisation. If the test measured the wrong thing, the assurance is false and harder to dislodge than no assurance at all. The legal test is not a statistic, and a tribunal will ask about disadvantage and justification, not about the chosen metric.
Warning signs
- No document explains why the metric was chosen.
- The same metric is used for every system.
- Legal and compliance had no part in setting the standard.
- Results are reported for single characteristics only.
Mitigating controls
Primary controls
- Fairness requirements and metric selection Directive
- Requires the choice of groups, metric and threshold to be justified against the legal test and the harm. For each system that affects people, the relevant groups, the legal test, the fairness measures and their thresholds are defined and justified before testing begins.
Supporting controls
- Independent assurance and internal audit of AI Detective
- Assurance checks that the stated approach was followed. Second-line review and internal audit cover AI governance and the highest-tier systems on a risk-based plan, test systems as well as documents, and track their findings to closure.
- Independent model validation Detective
- Independent validation challenges the choice. A function independent of the developers tests each model's conceptual soundness, data, performance and limitations before approval and periodically afterwards, to a depth set by the model's tier.
Framework and clause references
| Framework | Clause | Title |
|---|---|---|
| Equality Act 2010 | Section 19 | Indirect discrimination The legal test the metric must track. |
| EU AI Act | Article 10 | Data and data governance Bias examination. |
| NIST AI Risk Management Framework (AI RMF 1.0) | MEASURE 1 | Appropriate methods and metrics are identified and applied Selection of appropriate metrics. |
Sources
- EU AI Act: Regulation (EU) 2024/1689 European Union (EUR-Lex) · official text
- NIST AI Risk Management Framework (AI RMF 1.0) NIST · framework
Related terms
How to cite
Axiom Verity (2026). Mis-specified fairness measurement. Reference Library, R-112. https://axiom-verity.com/risks/mis-specified-fairness-measurement. Reviewed 2026-11-11.
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