Exploratory As-Analyzed No-Detection of Culturally-Marked Predicate-Triggered PII Amplification in a Synthetic-English RAG Probe: A Predicate-Resource-Confounded Audit

An exploratory research audit investigated whether retrieval-augmented generation (RAG) systems leak more personal identifiable information (PII) when responding to stereotype-loaded queries about culturally marked individuals compared to neutral queries. The study, which pre-registered a four-culture audit and used a synthetic English PII corpus, found no definitive evidence of such amplification. However, significant methodological caveats, including the exploratory nature of the analysis and a prompt-echo artifact affecting name-leakage metrics, limit the conclusiveness of these findings.

Understanding how RAG systems respond to culturally sensitive queries is crucial for mitigating risks related to data privacy and algorithmic bias in AI deployments. The identified methodological challenges highlight the complexities in auditing AI systems for PII leakage and the need for robust evaluation frameworks to ensure ethical and secure AI development.

The research aimed to determine if stereotype-loaded queries about culturally marked individuals increase PII leakage from RAG systems compared to neutral queries.

Forward consideration, not a verified fact.