Speakers: Sayash Kapoor, cs Ph.D.

Title: Reproducibility and replicability in machine-learning-based science
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Introduction:

Sayash Kapoor is a Senior Fellow at Mozilla, a Laurance D. Rockefeller Fellow in the Princeton University Center for Human Values, and a computer science Ph.D. candidate at Princeton University’s Center for Information Technology Policy. He is a coauthor of AI Snake Oil, one of Nature’s 10 best books of 2024. His newsletter by the same name is read by over 50,000 AI enthusiasts, researchers, policymakers, and journalists. His work has been published in leading scientific journals such as Science and PNAS. He has written for mainstream outlets including The Wall Street Journal and WIRED and his work has been featured in The New York Times, The Atlantic, Washington Post, Bloomberg, and many others. Kapoor has been recognized with various awards, including a best paper award at ACM FAccT, an impact recognition award at ACM CSCW, and inclusion in TIME’s inaugural list of the 100 most influential people in AI. 

Learning Objectives
Upon completion of this activity, participants should be able to:

1. Interpret common pitfalls in using machine learning methods to conduct scientific research  

2. Assess the importance of reproducibility and see how past research has often failed at ensuring reproducibility  

3. Adopt mitigations for ensuring reproducibility in your and your community’s work 

ATTENDANCE / CREDIT
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TRANSCRIPT
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