Two approaches to reducing privacy leaks in AI

As AI agents become more autonomous in handling tasks for users, it’s crucial they adhere to contextual norms around what information to share—and what to keep private. The theory of contextual integrity frames privacy as the appropriateness of information flow within specific social contexts. Applied to AI agents, it means that what they share should fit the situation: who’s involved, what the information is, and why it’s being shared.

For example, an AI assistant booking a medical appointment should share the patient’s name and relevant history but not unnecessary details of their insurance coverage. Similarly, an AI assistant with access to a user’s calendar and email should use available times and preferred restaurants when making lunch reservations. But it should not reveal personal emails or details about other appointments while looking for suitable times, making reservations, or sending invitations. Operating…