Abstract

As machine learning systems become larger and more opaque, our relationship to them increasingly shifts from inspection to interaction. We evaluate, audit, benchmark, red-team, and delegate to systems whose internal behavior we cannot fully characterize. This may seem like a new challenge created by modern AI, but in many ways it is deeply aligned with longstanding themes in theoretical computer science.

This talk argues, through examples drawn from privacy auditing and delegation in principal-agent settings, that many areas of TCS can be viewed as studying reliable interaction with black boxes under limited observability. The goal is not always to open the black box, but to understand what guarantees remain possible when we cannot.

Video Recording