EU AI Act Article 10 compliance checker
14 questions, each mapped to a specific Article 10 obligation. Score your training data governance against them. Browser-only, nothing logged, PDF report downloadable in one click.
1. For every training dataset, can you produce a documented chain of custody from collection through preprocessing?
Article 10(2)(b) requires traceability of training data origins. A supplier statement that the data is clean does not satisfy this on its own.
2. Can you link each consent record to specific training data points (not just bulk policies)?
Article 10(2)(b) data-point-level provenance includes consent linkage where personal data is involved. A standalone privacy policy is insufficient.
3. For third-party data sources, do you have contractual evidence of their Article 10 compliance?
Article 10(2)(e) extends provenance obligations to upstream data providers. Your documentation is only as complete as your least documented vendor.
4. Have you measured and documented your training data demographics against the intended deployment population?
Article 10(3) requires representativeness analysis, measured against the population the system will serve.
5. Is bias evaluation automated as part of your training pipeline, with results pre-dating model training timestamps?
Article 10(3) requires examination "in view of the intended purpose". Manual quarterly reviews do not meet a notified body audit trail.
6. For statistically under-represented groups, do you have a documented mitigation strategy (additional data collection, weighting, evaluation flags)?
Article 10(4) requires that gaps be examined, not just identified. The output of bias analysis must drive collection or mitigation actions.
7. For datasets containing biometric or special-category data (Art 9 GDPR), is access logged at the per-query level?
Article 10(5) requires access-trail evidence for sensitive data. A description of who is allowed access does not replace a log of who had it.
8. Can you produce, on demand, a list of every individual or service that has accessed a specific training dataset?
Article 10(5) audit-ability requires accountability per access event. Aggregate access logs are insufficient for individual subject rights enforcement.
9. For each model, is the "design choices" rationale (architecture, hyperparameters, data selection) documented and version-controlled?
Article 10(2)(a) requires design-choice traceability. The model registry must answer "why this architecture, why this data" with audit-grade specificity.
10. Are all data preprocessing operations (normalisation, filtering, augmentation) logged with parameters, version, and timestamp?
Article 10(2)(d) requires that data preparation is reproducible. A notified body asks "show me the pipeline that produced this checkpoint."
11. Is annotation methodology documented for every labelled dataset: guidelines, annotator qualifications and inter-annotator agreement (IAA) scores?
Article 10(2)(g) requires methodology documentation. Naming who did the annotation does not describe how it was done.
12. For each model version in production, can you produce the exact training dataset (or its content-hash) that produced it?
Article 10(2)(f) requires reproducibility. Drift between "what was trained" and "what we think was trained" is the most common audit failure.
13. When training data is updated, is there a defined process for re-evaluating bias / representativeness without re-training the model?
Article 10(4) implies ongoing examination as datasets evolve. Compliance is not a one-time event at training-time.
14. Are data collection operations themselves documented (where, when, by whom, under what conditions)?
Article 10(2)(c) requires collection-operation documentation. Naming the platform the data came through does not document how it was collected.
How this tool works
Each of the 14 questions is mapped to a specific clause of Article 10 of Regulation (EU) 2024/1689 (EU AI Act). The clause reference (e.g., Art 10(2)(b)) is shown on every question so you can verify our reading against the regulation.
Questions are grouped into five domains:
- Provenance & Data Lineage (Art 10.2-3): chain of custody, consent linkage, third-party vendor compliance
- Bias & Representativeness (Art 10.3-4): demographic analysis, automated bias evaluation, mitigation for under-represented groups
- Security & Access Control (Art 10.5): per-query access logging, dataset access trails
- Documentation & Methodology (Art 10.2(a), 10.2(d), 10.2(g)): design choices, preprocessing logs, annotation methodology
- Lifecycle & Iteration (Art 10.2(c), 10.2(f), 10.4): training-set reproducibility, re-evaluation on data changes, collection operation documentation
Scoring: each question has 4 response tiers worth 0-3 points. Maximum 42 points. Result thresholds:
- 85-100% Documented throughout
- 65-84% Mostly documented
- 40-64% Documentation gaps
- 0-39% Little documentation
Privacy
The tool is browser-only. No responses are transmitted to YPAI or any third party. The PDF report is generated locally using pdf-lib.
YPAI does log aggregate page visits via privacy-preserving analytics. Your individual responses are never tracked or stored.
Want to verify our reading?
The official text of Regulation (EU) 2024/1689 is at EUR-Lex. Article 10 is the canonical reference. For an engineering-focused walkthrough of the obligations, see our companion article: EU AI Act Article 10: What Engineers Must Actually Build.
Cite this tool
YPAI Research (2026). EU AI Act Article 10 Compliance Checker.
YPAI. https://ypai.ai/tools/eu-ai-act-checker/Training data for high-risk AI providers
YPAI builds Article 10 compliant speech and multimodal training data for high-risk AI providers: full data lineage, GDPR Article 9 consent frameworks, EU data residency, audit-grade documentation. If your assessment surfaced provenance, consent, or documentation gaps, that is what we fix.
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