Protecting Human Creators from Generative AI: Experience, Surprises and Ongoing Challenges
Generative AI is rapidly reshaping the world we live in. Today, economic motives are shaping messaging and perception around AI’s limitations and harms, posing ethical questions most of us are ill-equipped to answer. It is clear that researchers in AI (and CS in general) must be more accountable for not just our research results, but how they are deployed in applications. Over the four years, my lab at UChicago has focused on a research agenda focused on protecting those most vulnerable to harms produced by generative AI, both intentional and not. In this talk, I share some of more valuable lessons we learned developing and deploying tools like Glaze, Nightshade, and experiences engaging with stakeholders from creators to legislators, unions, and lawyers. I’ll share thoughts on what it’s like to drive a research agenda driven by stakeholders, and what I see as pressing challenges for researchers interested in advancing ethical AI.
Ben Zhao is Neubauer Professor of Computer Science at University of Chicago. He completed his Ph.D. at U.C. Berkeley (2004), and B.S. from Yale (1997). He is a Fellow of the ACM, TED/AI speaker, and a recipient of the NSF CAREER award, MIT Technology Review's TR-35 Award (Young Innovators Under 35), TIME100/AI (100 Most Influential people in AI), USENIX Internet Defense Prize, Concept Art Association's Community Impact Award, ComputerWorld Magazine's Top 40 Technology Innovators award, IEEE ITC Early Career Award, multiple best/distinguished/influential paper awards, and Faculty awards from Google, Amazon, and Facebook. His work has been covered by many media outlets including New York Times, CNN, NBC, ABC, BBC, MIT Tech Review, Wall Street Journal, Forbes, and New Scientist. His current research focuses on mitigating the harms of misused AI on human creatives. He served as TPC (co-)chair for the World Wide Web conference (WWW 2016), ACM Internet Measurement Conference (IMC 2018), and Steering Committee of IEEE Conference on Secure and Trustworthy Machine Learning (SaTML 2023-2026).
Agenda (Chicago Time):
6:30 – 6:45 PM — Introductions
6:45 – 7:30 PM — Talk
7:30 – 8:00 PM — Q&A and Networking
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