Reproduce “Operationalizing Contextual Integrity in Privacy-Conscious Assistants” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger and 5 more
Google DeepMind and Google Research developed and evaluated mechanisms enabling privacy-conscious AI assistants to adhere to user privacy expectations when mediating information-sharing flows. The research operationalized Contextual Integrity (CI) by introducing an Information Flow Card (IFC) and demonstrated that CI-based reasoning supervisors significantly reduce privacy leakage in form-filling tasks while maintaining high utility.
Key insights
- Contextual Integrity provides a suitable framework for grounding the design of AI assistants in societal privacy norms, moving beyond simplistic public/private distinctions.
- Decomposing the information-sharing decision into a supervisory module, particularly one guided by CI principles (IFC), significantly enhances privacy control and transparency in LLM-based agents.
- Human annotations are critical for eliciting and establishing context-dependent privacy norms, providing a robust, data-driven foundation for evaluating AI assistant privacy behavior.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.