Operationalizing Contextual Integrity in Privacy-Conscious Assistants

• URL: http://arxiv.org/abs/2408.02373v2
• Date: Fri, 13 Sep 2024 13:09:41 GMT
• Title: Operationalizing Contextual Integrity in Privacy-Conscious Assistants
• Authors: 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,
• Abstract summary: We propose to operationalize contextual integrity (CI) to steer advanced AI assistants to behave in accordance with privacy expectations. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of human annotations of common webform applications.
• Score: 34.70330533067581
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramatically with access to user information including emails and documents, this raises privacy concerns about assistants sharing inappropriate information with third parties without user supervision. To steer information-sharing assistants to behave in accordance with privacy expectations, we propose to operationalize contextual integrity (CI), a framework that equates privacy with the appropriate flow of information in a given context. In particular, we design and evaluate a number of strategies to steer assistants' information-sharing actions to be CI compliant. Our evaluation is based on a novel form filling benchmark composed of human annotations of common webform applications, and it reveals that prompting frontier LLMs to perform CI-based reasoning yields strong results.

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