Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric

Understand

As large language models (LLMs) are integrated into sociotechnical systems, it is crucial to examine the privacy biases they exhibit.

• We define privacy bias as the appropriateness value of information flows in responses from LLMs.
• A deviation between privacy biases and expected values, referred to as privacy bias delta, may indicate privacy violations.
• As an auditing metric, privacy bias can help (a) model trainers evaluate the ethical and societal impact of LLMs, (b) service providers select context-appropriate LLMs, and (c) policymakers assess the appropriateness of privacy biases in deployed LLMs.