LR-301e · Module 1

AI-Specific Evidence Requirements

3 min read

AI compliance frameworks require evidence types that traditional compliance programs do not produce. Model documentation, bias testing results, transparency disclosures, human oversight records, and AI impact assessments are artifacts specific to AI governance. Producing these artifacts requires integration with the AI system itself — the evidence comes from the model pipeline, not from the compliance office.

Do This

  • Generate model documentation automatically from the model registry — version, training data description, performance metrics, and deployment configuration
  • Capture bias testing results as structured evidence artifacts with methodology, metrics, and pass/fail criteria documented
  • Record every instance of human oversight — which decisions were reviewed, by whom, what action was taken — as compliance evidence

Avoid This

  • Write model documentation manually after deployment — manual documentation drifts from reality and lacks contemporaneous credibility
  • Run bias tests without documenting methodology — test results without methodology are not auditable evidence
  • Claim human oversight exists without recording the instances — an unrecorded oversight practice is an unverifiable one