Learn how to craft disclosure-centric AI safety policies that emphasize reporting, whistleblower protections, and public accountability.
Successful adoption requires buy-in across government, industry, civil society, and the research community.
1. **Scenario Planning:** Present hypothetical incidents (e.g., misaligned autonomous agent, biased credit model) and discuss how transparency requirements would respond.
2. **Role Mapping:** Identify responsibilities for regulators, organizations, auditors, and the public.
3. **Feedback Capture:** Use structured templates to gather concerns, suggestions, and resource needs.
4. **Action Commitments:** Assign ownership for next steps—drafting language, building portals, training staff.
Document outputs to refine policy drafts. Repeat sessions as the policy matures to maintain alignment.