Political Economy of Reinforcement Learning Systems (PERLS)

Thomas Gilbert · Stuart J Russell · Tom O Zick · Aaron Snoswell · Michael Dennis

Abstract Workshop Website
Tue 14 Dec, 4 a.m. PST


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).

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