Summary of Operationalizing Contextual Integrity in Privacy-conscious Assistants, by Sahra Ghalebikesabi et al.
Operationalizing Contextual Integrity in Privacy-Conscious Assistants
by Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi, Itay Yona, Ilia Shumailov, Aneesh Pappu, Chongyang Shi, Laura Weidinger, Robert Stanforth, Leonard Berrada, Pushmeet Kohli, Po-Sen Huang, Borja Balle
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper explores the development of advanced AI assistants that can autonomously perform complex tasks on behalf of users. These assistants combine large language models (LLMs) with tool access, enabling them to process vast amounts of user information, including emails and documents. However, this raises concerns about privacy breaches when assistants share inappropriate information without user supervision. To address these concerns, the authors propose a framework called contextual integrity (CI), which aims to ensure that assistants’ information-sharing actions align with users’ privacy expectations. The paper evaluates various strategies for steering assistants towards CI compliance using a novel benchmark composed of human-annotated webform applications. The results show that prompting LLMs to perform CI-based reasoning yields strong performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The proposed AI system helps people by doing tasks on their behalf, like filling out forms. This is useful but raises concerns about privacy. What if the AI shares personal information without permission? To solve this problem, researchers propose a way called contextual integrity that ensures AI assistants follow users’ expectations. They tested different methods to make AI systems behave in a way that respects user privacy and found that using large language models to reason about privacy works well. |
Keywords
» Artificial intelligence » Prompting
