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Tom Zick earned her PhD from UC Berkeley and is currently pursuing her JD at Harvard. Her research bridges between AI ethics and law, with a focus on how to craft safe and equitable policy surrounding the adoption of AI in high-stakes domains. In the past, she has worked as a data scientist at the Berkeley Center for Law and Technology, evaluating the capacity of regulations to promote open government data. She has also collaborated with graduate students across social science and engineering to advocate for pedagogy reform focused on infusing social context into technical coursework. Outside of academia, Tom has crafted digital policy for the City of Boston as a fellow for the Mayor’s Office for New Urban Mechanics and developed responsible AI resources for founders as a VC fellow at Bloomberg BETA. Her current research centers on the near term policy concerns surrounding reinforcement learning.


Projects & Tools

Responsible Generative AI: Accountable Technical Oversight

Generative AI is at a tipping point of adoption and impact, much like that of machine learning and AI years ago. But this time the stakes, regulatory environment, potential social…


News

May 31, 2023

Exploring the Impacts of Generative AI on the Future of Teaching and Learning

In December 2022, researchers from BKC, OpenAI, and Khan Academy and other experts gathered to discuss the impacts of generative AI on teaching and learning.


Community

EdTech Magazine

How Personhood Credentials Could Impact Higher Education

This new type of identity verification aims to separate humans from artificial intelligence.

Tom Zick weighs in on the utility of personhood credentials for combatting AI in higher education.

Nov 4, 2024

Personhood Credentials

Artificial intelligence and the value of privacy-preserving tools to distinguish who is real online

Wendy Seltzer and Tom Zick consider the efficacy of implementing "personhood credentials" as means to deter bad actors online while maintaining users' anonymity.

Aug 26, 2024
UC Berkeley Center for Long-Term Cybersecurity

Choices, Risks, and Reward Reports

Reinforcement learning could be the most promising path to artificial general intelligence. But Tom Zick and a team of other researchers urge caution in a new report.

Feb 1, 2022
BKC Medium Collection

Between Games and Apocalyptic Robots

Tom Zick considers the near-term societal risks of reinforcement learning

Apr 16, 2021