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Workshop
Political Economy of Reinforcement Learning Systems (PERLS)
Thomas Gilbert · Stuart J Russell · Tom O Zick · Aaron Snoswell · Michael Dennis

Tue Dec 14 04:00 AM -- 01:45 PM (PST) @
Event URL: https://perls-workshop.github.io/ »

Sponsored by the Center for Human-Compatible AI at UC Berkeley, and with support from the Simons Institute and the Center for Long-Term Cybersecurity, we are convening a cross-disciplinary group of researchers to examine the near-term policy concerns of Reinforcement Learning (RL). RL is a rapidly growing branch of AI research, with the capacity to learn to exploit our dynamic behavior in real time. From YouTube’s recommendation algorithm to post-surgery opioid prescriptions, RL algorithms are poised to permeate our daily lives. The ability of the RL system to tease out behavioral responses, and the human experimentation inherent to its learning, motivate a range of crucial policy questions about RL’s societal implications that are distinct from those addressed in the literature on other branches of Machine Learning (ML).

Author Information

Thomas Gilbert (UC Berkeley)
Stuart J Russell (UC Berkeley)
Tom O Zick (Harvard)

Tom Zick earned her PhD from UC Berkeley and  is a current fellow at the Berkman Klein Center for Internet and Society 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. Her current research centers on the near term policy concerns surrounding reinforcement learning.

Aaron Snoswell (Queensland University of Technology)

Aaron is a research fellow in computational law at the Australian Research Council Centre of Excellence for Autonomous Decision Making and Society. With a background in cross-disciplinary mechatronic engineering, Aaron’s Ph.D. research developed new theory and algorithms for Inverse Reinforcement Learning in the maximum conditional entropy and multiple intent settings. Aaron’s ongoing work investigates technical measures for achieving value alignment for autonomous decision making systems, and legal-theoretic models for AI accountability.

Michael Dennis (University of California Berkeley)

Michael Dennis is a 5th year grad student at the Center for Human-Compatible AI. With a background in theoretical computer science, he is working to close the gap between decision theoretic and game theoretic recommendations and the current state of the art approaches to robust RL and multi-agent RL. The overall aim of this work is to ensure that our systems behave in a way that is robustly beneficial. In the single agent setting, this means making decisions and managing risk in the way the designer intends. In the multi-agent setting, this means ensuring that the concerns of the designer and those of others in the society are fairly and justly negotiated to the benefit of all involved.

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