Privacy Reasoning in Ambiguous Contexts
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
We study the ability of language models to reason about appropriate
information disclosure - a central aspect of the evolving field of agentic
privacy. Whereas previous works have focused on evaluating a model's ability to
align with human decisions, we examine the role of ambiguity and missing
context on model performance when making information-sharing decisions. We
identify context ambiguity as a crucial barrier for high performance in privacy
assessments. By designing Camber, a framework for context disambiguation, we
show that model-generated decision rationales can reveal ambiguities and that
systematically disambiguating context based on these rationales leads to
significant accuracy improvements (up to 13.3\% in precision and up to 22.3\%
in recall) as well as reductions in prompt sensitivity. Overall, our results
indicate that approaches for context disambiguation are a promising way forward
to enhance agentic privacy reasoning.
