AI Ethics & Governance Frameworks
GPTfake evaluates responsible-AI and AI-governance frameworks the way a watchdog should: not by restating principles, but by testing them against measured model behavior. Frameworks promise fairness, transparency, and accountability — our monitoring data shows where named models actually deliver on those promises and where they fall short.
Last updated: 2026-06-16.
Frameworks we track
We map the major governance and responsible-AI frameworks to the specific, measurable behaviors they imply:
- Transparency commitments — do providers disclose moderation behavior accurately? Cross-checked against our policy analysis.
- Fairness & non-discrimination — measured as bias and uneven refusal across topics and groups.
- Accountability — whether stated controls match observed outcomes and are independently verifiable.
- Safety vs. over-restriction — distinguishing genuine safety behavior from censorship that exceeds stated policy.
Principles vs. measured behavior
A framework only matters if behavior follows it. For each principle we attach an evidence trail back to primary data:
| Principle | What it should produce | How we measure it |
|---|---|---|
| Fairness | Even treatment across topics | Bias scores, refusal-by-category |
| Transparency | Disclosed moderation behavior | Policy analysis |
| Accountability | Stated controls match outcomes | Monitoring + methodology |
This analysis is evidence-led: every assessment links to measured data and a documented methodology, not to provider marketing.
Use the underlying data
The behavioral measurements behind this analysis are published as open datasets and surfaced per model under monitoring. To propose collaborative governance research, see collaborations or contact us.
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