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Beyond Ads: Sequential Decision-Making Algorithmsin Public Policy
Peter Henderson · Brandon Anderson · Daniel Ho

We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in the public sector. While such algorithms have been heavily studied in settings that are suitable for the private sector (e.g., online advertising), the public sector could greatly benefit from these approaches, but poses unique methodological challenges for machine learning. We highlight several applications of sequential decision-making algorithms in regulation and governance, and discuss areas for further research which would enable them to be more widely applicable, fair, and effective. In particular, ensuring that these systems learn rational, causal decision-making policies can be difficult and requires great care. We also note the potential risks of such deployments and urge caution when conducting work in this area. We hope our work inspires more investigation of public-sector sequential decision making applications, which provide unique challenges for machine learning researchers and can be socially beneficial.

Author Information

Peter Henderson (Stanford University)
Brandon Anderson (Stanford Law)
Daniel Ho (Stanford Law)

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