AI Content Policy & Regulation Analysis
GPTfake compares what AI providers say their content policies are against what their models actually do under standardized testing. Published usage policies are aspirational documents; our monitoring data shows where observed behavior diverges from them — and where regulation like the EU AI Act will bite.
Last updated: 2026-06-16. Comparisons draw on our open datasets and monitoring data.
Stated policy vs observed behavior
For each model we line up three things and look for gaps:
- Official policy documentation — the provider’s published usage and content policies.
- Observed behavior — measured refusal and bias outcomes on our standardized prompt set.
- The delta — topics that policy says are allowed but the model refuses (over-restriction), and topics policy restricts but the model answers (under-enforcement).
These deltas are the substance of accountability reporting: they are documented, reproducible, and traceable to specific prompts and dates via the monitoring methodology.
| Provider | Stated stance | Observed behavior | Source |
|---|---|---|---|
| OpenAI / ChatGPT | Published usage policy | Measured refusal rate by category | ChatGPT monitoring |
| Anthropic / Claude | Constitutional AI principles | Measured refusal & bias profile | Claude monitoring |
| Google / Gemini | Content & regional policy | Regional response variation | Gemini monitoring |
Policy comparisons describe documented gaps between stated rules and measured behavior. Every claim links to its underlying dataset and sample size; figures here are illustrative until a dataset version is attached.
EU AI Act & regulation
The EU AI Act introduces transparency and risk obligations that make the policy-vs-behavior gap legally material. Our analysis maps observed model behavior to the obligations most relevant to censorship and bias:
- Transparency — whether providers disclose content-moderation behavior accurately enough for users to understand restrictions.
- Risk management — whether documented refusal/bias patterns are consistent with stated risk controls.
- Fundamental rights — where systematic over-restriction or biased refusal could affect access to information.
We track regulatory developments in our dated reports, which carry citable URLs for journalists and policy researchers.
Cross-platform comparison
Policies read very differently across providers, but measured behavior often converges. Putting the models side by side shows whether divergent stated policies actually produce divergent outcomes. See the compare hub for head-to-head refusal and bias data, and the longitudinal studies for how these gaps move over time.
To propose policy-focused collaborative research, see collaborations or contact us.
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