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Workshop: Political Economy of Reinforcement Learning Systems (PERLS)

Power and Accountability in RL-driven Environmental Policy

Melissa Chapman · Caleb Scoville · Carl Boettiger


Machine learning (ML) methods already permeate environmental decision-making, from processing high-dimensional data on earth systems to monitoring compliance with environmental regulations. Of the ML techniques available to address pressing environmental problems (e.g., climate change, biodiversity loss), Reinforcement Learning (RL) may both hold the greatest promise and present the most pressing perils. This paper explores how RL-driven policy refracts existing power relations in the environmental domain while also creating unique challenges to ensuring equitable and accountable environmental decision processes. We focus on how RL technologies shift the distribution of decision-making, agenda-setting, and ideological power between resource users, governing bodies, and private industry.

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