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Workshop: Political Economy of Reinforcement Learning Systems (PERLS)

Deciding What's Fair: Challenges of Applying Reinforcement Learning in Online Marketplaces

Andrew Chong


Reinforcement learning (RL) techniques offer a versatile and powerful extension to the toolkit for computer scientists and marketplace designers for their use in online marketplaces. As the use of these techniques continues to expand, their application in online marketplaces raise questions of their appropriate use, particularly around issues of fairness and market transparency. I argue that the use of RL techniques, alongside similar calls in domains such as automated vehicle systems, is a problem of sociotechnical specification that faces a set of normative and regulatory challenges unique to marketplaces. I provide a selective overview of the RL literature as applied to markets to illustrate challenges associated with the use of RL techniques in online marketplaces. I conclude with a discussion of capacity-building in research and institutions that is required in order for benefits from algorithmically managed marketplaces to be realized for stakeholders and broader society.

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