Toward evidence-based AI in science, law, and safety | Sayash Kapoor
Skills:
Research Methods90%Reading ML Papers80%AI Alignment Basics80%AI Ethics & Policy80%AI Safety Engineering70%
In this talk, Sayash Kapoor, PhD candidate at Princeton University's Center for Information Technology Policy, explores the promise and perils of AI through the lens of empirical evidence. As AI tools are rapidly adopted across scientific and legal domains—with some companies ambitiously claiming they can automate all legal and scientific tasks—concerns about over-optimism and catastrophic risks like biosecurity and cybersecurity loom large.
Sayash presents three case studies that underscore the importance of rigorous, evidence-based analysis.
First, he examines how attempts to automate science have faced major reproducibility failures, with current models often unable to replicate results even when provided with code and data.
Second, he highlights how legal AI applications that could significantly reshape the legal profession are also the hardest to evaluate, making them especially vulnerable to exaggerated claims.
Third, he discusses how to meaningfully assess AI-related risks by examining the marginal risk AI poses beyond existing technologies. The talk concludes with a broader reflection on how to conduct robust empirical analysis in the AI domain.
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