As AI becomes increasingly integrated into both the private and public sectors, challenges around AI safety and accountability have arisen. There is a growing, compelling body of work around the legal and societal challenges that come with AI, but there is a gap in our rigorous understanding of these problems. In this talk, I dive deep into a few topics in AI safety and accountability. We will discuss AI supply chains (the increasingly complex ecosystem of AI actors and components that contribute to AI products) and study how AI supply chains complicate machine learning objectives. We'll then shift our discussion to AI audits and evidentiary burdens in cases involving AI. Using Pareto frontiers as a tool for assessing performance-fairness tradeoffs, we will show how a closed-form expression for performance-fairness Pareto frontiers can help plaintiffs (or auditors) overcome evidentiary burdens or a lack of access in AI contexts. I'll conclude with a longitudinal study of LLMs during the 2024 US election season. If time permits, we may touch on formal notions of trustworthiness.

Sarah Cen is an Assistant Professor at Carnegie Mellon University's Departments of ECE & EPP. Her research is interdisciplinary and inspired by tools and frameworks in machine learning, economics, law, and policy. She has ongoing work on algorithmic auditing, AI supply chains, due process for AI determinations, risk under the EU AI Act, and formalizing trustworthy algorithms. Previously, Sarah was a postdoc at Stanford with Prof. Percy Liang in Computer Science and Prof. Daniel Ho in the Stanford Law School. Sarah received her BSE in Mechanical Engineering from Princeton University and Master's in Engineering Science (Robotics) from Oxford University, where she worked on autonomous vehicles.