Technical Papers on AI Censorship Detection

GPTfake’s technical papers document the methods behind our monitoring: how we detect refusals, score bias, and catch silent model changes at scale. These are the reproducible, methodology-grade write-ups aimed at AI researchers and engineers who want to verify, extend, or build on our work.

Last updated: 2026-06-16.

Methods we publish

• Response pattern classification — taxonomy of refusals, deflections, and partial answers, and the classifier that labels them.
• NLP-based censorship detection — automated detection of refusal language across phrasings and languages.
• Semantic similarity analysis — measuring how a model’s answer drifts from a reference over time and across versions.
• Change-detection algorithms — flagging statistically significant shifts in behavior to surface silent updates.
• Red-teaming protocols — adversarial prompting to probe guardrails systematically rather than anecdotally.

Reproducibility

Every technique here is built to be reproduced from open inputs:

• Methods are documented in the monitoring methodology.
• Inputs and outputs ship as open datasets (prompt libraries, response classifications).
• Results are surfaced per model under monitoring so any claim is verifiable from raw data.

Build on this work

Researchers and developers can request method details or datasets via collaborations, or contact us to propose a joint technical study.