Human-Centred LLM Privacy Audits: Findings and Frictions
Large language models (LLMs) learn statistical associations from massive training corpora and user interactions, and deployed systems can surface or infer information about individuals. Yet people lack practical ways to inspect what a model associates with their name. We report interim findings from an ongoing study and introduce LMP2, a browser-based self-audit tool. In two user studies ($N_{total}{=}458$), GPT-4o predicts 11 of 50 features for everyday people with $\ge$60\% accuracy, and participants report wanting control over LLM-generated associations despite not considering all outputs privacy violations. To validate our probing method, we evaluate eight LLMs on public figures and n...
| Authors | Dimitri Staufer, Kirsten Morehouse, David Hartmann et al. |
|---|---|
| Primary category | cs.HC |
| Published | 12 March 2026 |
| Last updated at source | 12 March 2026 |
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