Full course coming soon

Documenting AI Systems for Compliance and Consumer Protection

7 chapters · 30 lessons

1. 1. Mapping Public Claims to Internal Evidence

   4 lessons

   Learn to identify the gap between external product language and the technical evidence needed to substantiate those claims.

2. 2. Designing Compliance-Ready Incident Records

   5 lessons

   Structure AI incident logs to serve debugging, learning, and regulatory review without creating unnecessary risk.

3. 3. Translating Technical Failures into User Impact

   4 lessons

   Convert model errors, data leaks, and agent misbehavior into user-centered impact assessments that inform notice and mitigation.

4. 4. Building Documentation Workflows That Scale

   5 lessons

   Create repeatable processes for logging, reviewing, and escalating AI safety issues across engineering, legal, and product teams.

5. 5. Preparing Evaluation Summaries for External Review

   4 lessons

   Package technical test results, red team findings, and mitigation plans into formats that auditors and regulators can assess.

6. 6. Vendor AI Systems and Third-Party Accountability

   4 lessons

   Extend documentation and evidence standards to procured AI tools, ensuring vendors meet your compliance obligations.

7. 7. Governance Signals and Executive Visibility

   4 lessons

   Design reporting mechanisms that surface unresolved AI risks to leadership and demonstrate accountability to regulators.