Use cases
GPTfake data is used wherever someone needs evidence — not anecdotes — about how AI models censor, bias, or reshape information. Here is how each of our three core audiences puts it to work, with the pages to start from.
For researchers
Academics use GPTfake’s reproducible data and open datasets to study AI behavior at scale.
- Longitudinal studies — track how a model’s refusal rate or bias drifts across versions over months. Start with longitudinal studies.
- Fairness audits — measure bias across demographic groups and topics using our bias detection methods and fairness metrics.
- Reproducibility — re-run our methodology against the published prompt library and compare.
- Open data — download daily monitoring data and prompt libraries from datasets.
For journalists
Reporters use GPTfake as a citable, independent source for stories about AI companies.
- Investigative angles — “is ChatGPT censored?” answered with data on ChatGPT monitoring.
- Head-to-heads — Claude vs ChatGPT censorship and the least-censored models make clean, rankable stories.
- Fresh findings — our dated Reports are built to be cited, each with a fixed URL and a “how to cite” block.
- Policy context — policy analysis compares stated rules to observed behavior.
For developers
Engineers integrate GPTfake data into their own products, monitoring, and compliance workflows.
- Model selection — compare censorship and bias before choosing a model; pull live numbers via the API.
- Drift alerts — set up webhooks to be notified when a model’s policy or refusal rate shifts.
- Cross-checks — use GPTfake as an independent second opinion alongside a provider’s own moderation API.
- Dashboards — build internal monitoring with the Python or JavaScript SDK; see the dashboard tutorial.
GPTfake