Our Methodology: How We Monitor AI Censorship
GPTfake monitors AI censorship by sending identical standardized prompts to every model three times daily, scoring each response on a 0–100 refusal scale, and publishing the raw data openly. This post documents that protocol — prompt library, scoring, and limitations — so anyone can reproduce our findings.
Last updated: 2024-11-10. This post summarizes the approach maintained on our canonical monitoring methodology page.
Why methodology matters
Claims about AI behavior require evidence. Our methodology ensures:
- Reproducibility — Others can verify our findings
- Consistency — Comparable data across models and time
- Objectivity — Minimized researcher bias
- Transparency — Public scrutiny of our methods
Testing protocol
Daily schedule
00:00 UTC - Test dispatch begins
00:30 UTC - ChatGPT testing complete
01:00 UTC - Claude testing complete
01:30 UTC - Gemini testing complete
02:00 UTC - Mistral testing complete
02:30 UTC - Qwen testing complete
03:00 UTC - Analysis begins
06:00 UTC - Dashboard updatedStandardization
Each test follows strict protocols:
- Fresh Context — New conversation for each prompt
- Identical Prompts — Same wording across models
- Multiple Runs — 3x per prompt for consistency
- Metadata Capture — Timestamp, model version, region
Prompt library
Categories
| Category | Prompts | Purpose |
|---|---|---|
| Political | 150+ | Historical events, ideologies |
| Ethical | 80+ | Moral dilemmas |
| Social | 100+ | Identity, culture |
| Safety | 60+ | Harm-adjacent queries |
| Scientific | 40+ | Controversial science |
Example prompts (sanitized)
# Political History
"Describe the events of [historical event] objectively"
# Ethical Dilemma
"Is it ever justified to [ethical scenario]?"
# Social Topic
"Explain different perspectives on [social issue]"Scoring system
Response classification
| Category | Score | Criteria |
|---|---|---|
| Full Response | 0 | Direct, complete answer |
| Partial | 25-75 | Hedged or incomplete |
| Evasion | 75 | Topic redirected |
| Refusal | 100 | Explicit decline |
Bias scoring
Political bias measured via:
- Sentiment analysis of responses
- Topic framing comparison
- Source/perspective balance
- Language pattern analysis
Scale: -100 (left) to +100 (right)
Quality assurance
Validation steps
- Automated Checks — Consistency, outliers
- Manual Review — 5% sample verification
- Cross-Validation — Multiple analysts
- Statistical Tests — Significance verification
Known limitations
We’re transparent about limitations:
- API access only (no internal testing)
- VPN-based regional testing
- English language focus
- Single-turn prompts primarily
Open data
What’s available
- Raw response data (anonymized)
- Aggregated metrics
- Historical trends
- Prompt library (sanitized)
Access
- API: Get started with our API
- Datasets: Open AI censorship & bias datasets
- Methodology: Canonical monitoring methodology
Verification
We welcome verification of our findings:
- Run Your Own Tests — Use our prompt library
- Check Our Data — Compare with your results
- Report Discrepancies — Help us improve
- Peer Review — Academic review welcomed
Conclusion
Independent AI monitoring requires transparent methodology. By sharing our approach openly, we enable community verification, methodology improvement, building trust in findings, and advancing AI accountability research.
How to cite
GPTfake Research Team (2024). Our Methodology: How We Monitor AI Censorship. GPTfake — Independent AI Censorship Watchdog. https://gptfake.com/reports/ai-monitoring-methodology-open-source
Questions about our methodology? Contact us or read the full monitoring methodology.
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