Overview
How might we design transparency report so people can trust and act on AI output?
When to use
- Perfect for enterprise AI, public-facing AI systems, and applications where periodic transparency reports build trust and demonstrate accountability.
When to skip
- Early prototypes with no meaningful metrics yet.
- Personal toys where org-level reporting is irrelevant.
- Reports so redacted they communicate nothing (publish a honest subset instead).
Rules
Marketing PDFs with no dates, methods, or limitations.
Hiding incident counts while boasting accuracy.
Annual reports that never link from the product UI.
Metrics that change definition each period without a changelog.
Evidence
| Product | Implementation |
|---|---|
| OpenAI | System and usage transparency materials for model behavior. |
| Google AI | Model and safety reporting surfaces for products. |
| Microsoft AI | Responsible AI reports tied to Copilot-era products. |
| Enterprise AI platforms | Admin dashboards summarizing overrides and incident rates. |