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CE Regulatory Compliance

Map calibrated_explanations capabilities to EU AI Act, GDPR, AI Liability Directive, and Product Liability Directive obligations for compliance documentation and presentation materials.

ID: cross-jurisdiction.regulatory.ce-regulatory-compliance Version: 0.1.0 License: BSD-3-Clause Author: Moffran Language: en Added: 2026-06-01

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CE Regulatory Compliance

You are mapping calibrated_explanations capabilities to EU regulatory obligations. This skill covers the EU AI Act, GDPR, AI Liability Directive (AILD), and the revised Product Liability Directive (PLD) as they apply to ML prediction systems.

This is NOT legal advice. This skill provides capability-to-article mappings based on the library's technical features. Legal interpretation requires qualified counsel.

Load references/regulation_capability_map.md for the full article-to-CE mapping across all four regulations.

Required references

docs/practitioner/playbooks/eu-ai-act-compliance.md — canonical compliance guide
CITATION.cff — paper references for mathematical guarantees

Use this skill when

Writing or reviewing compliance documentation for a CE-powered system.
Preparing presentations on CE and regulatory compliance.
Answering "how does CE satisfy Article X?" questions.
Identifying gaps where CE alone is insufficient and additional controls are needed.

Quick reference: CE capabilities and their regulatory relevance

CE capability	Method(s)	Regulatory relevance
Per-instance factual rules	`explain_factual()`, `print_rules()`	Transparency (AI Act Art. 13), Right to explanation (GDPR Art. 22)
Counterfactual alternatives	`explore_alternatives()`	Right to explanation (AI Act Art. 50, GDPR Recital 71)
Calibrated probabilities	`predict_proba(uq_interval=True)`	Accuracy declaration (AI Act Art. 15), Risk quantification (Art. 9)
Uncertainty intervals	Coverage-guarantee bounds from Venn-Abers/CPS	Robustness documentation (AI Act Art. 15), Burden of proof (AILD Art. 4)
Reject/escalation policy	`RejectPolicy.FLAG`, straddle/width gates	Human oversight (AI Act Art. 14)
Mondrian calibration	`bins=` parameter on explain/predict	Bias examination (AI Act Art. 10), Non-discrimination (GDPR Art. 22(3))
JSON audit payload	`to_json()`, `to_json_stream()`	Record-keeping (AI Act Art. 12), Technical docs (Art. 11 + Annex IV)
Narrative output	`to_narrative(expertise_level=...)`	Plain-language explanation (AI Act Art. 50, GDPR Recital 71)
Schema validation	`validate_payload()`	Audit evidence integrity (AI Act Art. 11)
Guarded explanations	`explain_guarded_factual()`	OOD detection for production (AI Act Art. 9)

Regulation-by-regulation overview

EU AI Act (Regulation (EU) 2024/1689)

The primary regulation. CE maps to 8 articles: Art. 9, 10, 11+Annex IV, 12, 13, 14, 15, and 50. Full article-by-article mapping with code examples is in docs/practitioner/playbooks/eu-ai-act-compliance.md.

Key strength: CE provides empirically valid coverage guarantees (not heuristic confidence scores), which is a stronger claim for Art. 15 accuracy documentation.

GDPR (Regulation (EU) 2016/679)

Relevant when ML predictions constitute automated individual decision-making:

Art. 22 — Right not to be subject to solely automated decisions with legal or significant effects. CE enables meaningful human-in-the-loop via uncertainty gates (RejectPolicy, interval width checks).
Art. 22(3) — Right to obtain human intervention, express a point of view, and contest the decision. CE's explore_alternatives() directly supports contestation by showing what would change the outcome.
Recital 71 — Right to obtain an explanation of the decision reached after assessment. CE's explain_factual() + to_narrative() produce per-instance explanations suitable for data subject access requests.
Art. 35 — Data Protection Impact Assessment (DPIA). CE's Mondrian calibration provides quantitative bias evidence for the DPIA.

AI Liability Directive (AILD — Directive proposal COM/2022/496)

Art. 3 — Presumption of causality: CE audit logs (to_json()) demonstrate compliance with care standards, helping rebut presumed causality.
Art. 4 — Disclosure and presumption of fault: CE payloads are pre-prepared, structured disclosure evidence. Failure to disclose = presumed fault.
A provider using CE can demonstrate transparency was provided, uncertainty was quantified, uncertain cases were escalated, and bias was examined.

Product Liability Directive (PLD — Directive (EU) 2024/2853)

Art. 4(4) — AI systems are "products." CE outputs are product safety artefacts.
Art. 7 — Defectiveness considers safety expectations. CE's uncertainty intervals document expected safety level at deployment time.
Art. 9(4) — Disclosure: CE audit payloads serve as pre-prepared evidence.

What CE does NOT cover

Gap	Regulation	Required additional control
Data quality certification	AI Act Art. 10(2)(a-e)	Data validation framework (Great Expectations, Soda)
Cybersecurity/adversarial robustness	AI Act Art. 15(3-5)	Adversarial testing (ART library), access controls
Conformity assessment	AI Act Art. 43-49	Notified body or internal assessment process
Post-market monitoring	AI Act Art. 72	Drift detection system (Evidently, WhyLabs)
Human reviewer training	AI Act Art. 14(4)(a)	Documented training programme
DPIA process	GDPR Art. 35	Legal/DPO-led impact assessment
Insurance/liability coverage	AILD	Organisational risk management
CE marking declaration	PLD Art. 4	Regulatory affairs process

Constraints

This skill provides capability mappings, not legal advice.
Always cite specific article numbers when making compliance claims.
Always cite concrete CE method names when claiming coverage.
Flag gaps honestly — partial coverage must be stated as such.
The AILD references are based on the proposed text and may change.
Refer to docs/practitioner/playbooks/eu-ai-act-compliance.md as the canonical detailed guide for AI Act compliance.