| name | ce-regulatory-compliance |
| title | CE Regulatory Compliance |
| description | Map calibrated_explanations capabilities to EU AI Act, GDPR, AI Liability Directive, and Product Liability Directive obligations for compliance documentation and presentation materials. |
| author | Moffran |
| author_url | https://github.com/Moffran/calibrated_explanations/tree/main/.claude/skills/ce-regulatory-compliance |
| license | BSD-3-Clause |
| version | 0.1.1 |
| execution_mode | open |
| jurisdiction | eu |
| practice | regulatory |
| language | en |
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 guideCITATION.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(), |
