BKC Spring Speaker Series Event
The promise of AI agents has led to claims of imminent and rapid adoption across fields. Companies have even promised to build AI agents that can automate all legal and scientific tasks. At the same time, there are concerns about their misuse leading to catastrophic risks such as bio and cybersecurity risks. In this talk, we will go over three case studies to foreground the importance of evidence-based AI analysis. First, while AI has been claimed to automate all of science, existing adoption has been plagued by severe reproducibility failures that lead to overoptimistic results across dozens of fields. Recent empirical work shows that current models fall well short of accomplishing far simpler tasks, such as reproducing a paper’s results even when the code and data are provided. Second, for legal applications, tasks that would lead to the most significant changes to the legal profession are also the ones most prone to overoptimism about AI capabilities, as they are harder to evaluate. Third, for analyzing safety risks, it is important to analyze the marginal risk of AI over and above existing technology to evaluate the effectiveness of policy interventions. We conclude with a discussion of how to effectively conduct empirical analysis of AI.
Speaker
Sayash Kapoor is a computer science Ph.D. candidate at Princeton University's Center for Information Technology Policy and a co-author of AI Snake Oil. His research focuses on the societal impact of AI. He is a recipient of a best paper award at ACM FAccT, an impact recognition award at ACM CSCW, and was included in TIME's inaugural list of the 100 most influential people in AI.
