Policy making should be informed by evidence, especially scientific evidence. But exactly how is a surprisingly tricky question. In this talk Narayanan will take a close look at the science-policy interface: how it works and how it should work. The talk will diagnose structural reasons why he believes the kind of evidence that science is good at producing is mismatched with the kind of evidence that’s useful for policy. Ignoring this mismatch leads to bad policy and weakens public trust in science. The talk will end by proposing paths toward a healthier relationship between science and policy.

This talk is a high-level overview of an early-stage book project and is an invitation to deeper 1-1 discussions.

Bio:

Arvind Narayanan is the director of CITP and a professor of computer science at Princeton University. He co-authored a textbook on fairness and machine learning and is currently co-authoring a book on AI snake oil. He led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes, and his doctoral research showed the fundamental limits of de-identification. Narayanan is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE), twice a recipient of the Privacy Enhancing Technologies Award, and thrice a recipient of the Privacy Papers for Policy Makers Award.

The recording of this talk is available to the Princeton University community by request. Please contact Jean Butcher at to request the video.

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