Bias Assessment Template (EU AI Act, Article 10)
A bias and fairness assessment template for EU AI Act Article 10 data governance: protected attributes, metrics, mitigations, special-category data rules.
Article 10 of the EU AI Act, Regulation (EU) 2024/1689, is the data-governance requirement for high-risk AI — and one of its specific commands is that providers examine their training, validation and testing datasets for possible biases that are likely to affect health and safety, have a negative impact on fundamental rights, or lead to discrimination prohibited under EU law. This template turns that command into a fillable record: the protected attributes you examined, the fairness metrics you measured, the biases you found, and the mitigations you applied. It is the provider's document — Article 10 sits with whoever develops the system and places it on the market.
A bias assessment is not a one-off ethics review bolted on at the end. Article 10(2)(f) and 10(2)(g) make bias examination and mitigation part of the data-governance practices that run through dataset design, collection and preparation. Treat this record as living evidence inside your Article 11 / Annex IV technical documentation, not a standalone memo.
What Article 10 actually requires on bias
Article 10 sets quality criteria for the datasets that feed a high-risk system. The bias-relevant parts are precise:
- Article 10(2)(f) — examination in view of possible biases that are likely to affect the health and safety of persons, have a negative impact on fundamental rights, or lead to discrimination prohibited under Union law, especially where data outputs influence inputs for future operations (feedback loops).
- Article 10(2)(g) — appropriate measures to detect, prevent and mitigate those identified biases.
- Article 10(3) — datasets must be relevant, sufficiently representative, and to the best extent possible free of errors and complete in view of the intended purpose.
The assessment below is structured to produce evidence against each of these.
Protected attributes and the special-category-data allowance
You cannot test for biased outcomes across groups without knowing the groups — which usually means processing data about protected characteristics (sex, racial or ethnic origin, and so on). That collides with GDPR Article 9, which restricts processing of special-category personal data. The EU AI Act resolves the tension narrowly. Article 10(5) permits providers to process special-category data exceptionally, to the extent strictly necessary for the purpose of detecting and correcting bias in high-risk systems — but only with safeguards: the bias cannot be detected by processing other (including synthetic or anonymised) data; the data is subject to technical limits on reuse and state-of-the-art security and privacy measures; access is strictly controlled and documented; and the special-category data is not transmitted to third parties or otherwise made available beyond the bias-correction purpose, and is deleted once the bias is corrected or the retention period ends.
In short: Article 10(5) is a permission, not a free pass. Document why you needed the protected attribute, that no less-intrusive route worked, and the GDPR safeguards you applied.
The fairness metrics — measure, do not assert
A credible bias assessment reports numbers, not adjectives. Pick metrics that match the decision the system makes, and record the disparity across groups:
| Metric | What it measures | When it is the right lens |
|---|---|---|
| Demographic parity | Equal positive-outcome rates across groups | Resource allocation, shortlisting |
| Equal opportunity | Equal true-positive rates across groups | When false negatives carry the harm (e.g. missed eligibility) |
| Predictive parity | Equal precision across groups | When acting on a positive prediction (e.g. a flag) |
| Error-rate balance | Comparable false-positive / false-negative rates | Safety-relevant misclassification |
No single metric is "fair" in the abstract — several are mathematically incompatible with each other. Choose deliberately, state why, and record the trade-off. That reasoning is itself the evidence Article 10(2)(f) asks for.
The template — fields to capture
1. System and dataset identification — system name, version, intended purpose; the training, validation and testing datasets examined; Annex III category.
2. Protected attributes examined — which attributes (sex, racial/ethnic origin, age, disability, etc.); how they were obtained or proxied; the Article 10(5) / GDPR Article 9 justification and safeguards where special-category data was processed.
3. Representativeness check (Article 10(3)) — whether each dataset is relevant and sufficiently representative of the population in the intended-use context; gaps and known under-representation.
4. Bias examination (Article 10(2)(f)) — the metrics computed; disparities found across groups; any feedback-loop risk where outputs influence future inputs.
5. Prohibited-discrimination check — whether any measured disparity could lead to discrimination prohibited under Union law; cross-reference to the relevant equality directive.
6. Mitigation (Article 10(2)(g)) — measures applied (re-sampling, re-weighting, threshold adjustment, additional data collection); residual disparity after mitigation; what could not be fixed and why.
7. GDPR linkage — DPIA reference (GDPR Article 35) where one exists; lawful basis; deletion of special-category data once bias correction is complete (Article 10(5)).
8. Review and sign-off — reviewer; date; trigger for re-assessment (new data, model retrain, drift).
Worked example: a provider of a hiring-shortlist system
Talentbrück GmbH, a provider with about 75 employees and roughly €14M turnover, develops a high-risk CV-shortlisting system (Annex III point 4, employment). To test for sex bias under Article 10(2)(f), it needs applicants' sex — special-category-adjacent data it would normally minimise. Relying on Article 10(5), it processes the attribute strictly to measure and correct bias, restricts access to two named data scientists, applies state-of-the-art security, and documents that synthetic data alone could not surface the disparity. The assessment finds an equal-opportunity gap: qualified women are shortlisted at a lower true-positive rate. Talentbrück re-weights the training data and adjusts the decision threshold, records the residual disparity, and deletes the protected-attribute dataset once correction is verified. The whole record drops into its Article 11 / Annex IV technical documentation, and a DPIA under GDPR Article 35 covers the personal-data side.
How Confir helps
Confir's data-and-technical-robustness assessment walks an Article 10 dataset through plain-English questions — representativeness, the Article 10(2)(f) bias examination, the Article 10(5) special-category-data conditions — and records the answers as structured evidence that feeds the Article 11 / Annex IV pack. It cross-maps the data-governance controls to GDPR so the AI Act and data-protection records reference each other rather than duplicating. The engine is deterministic and rule-based: the same dataset facts produce the same finding every time.
Related guides
- Article 10 data and data governance
- Data governance under the EU AI Act
- Data governance policy template
- GDPR and EU AI Act intersection
- Article 11 technical documentation
- Annex IV technical documentation template
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