Beyond Ads: Sequential Decision-Making Algorithms in Law and Public Policy

TL;DR

This paper discusses the potential and challenges of applying sequential decision-making algorithms in law and public policy, emphasizing the need for new methods to address domain-specific complexities and ethical considerations.

Contribution

It identifies unique methodological challenges in law and policy for sequential decision algorithms and outlines research directions to adapt these methods beyond advertising applications.

Findings

Highlights the distinct challenges of applying algorithms in law and policy

Proposes research directions for causal and rational decision-making in public sector

Warns about risks and harms associated with algorithm deployment in governance

Abstract

We explore the promises and challenges of employing sequential decision-making algorithms -- such as bandits, reinforcement learning, and active learning -- in law and public policy. While such algorithms have well-characterized performance in the private sector (e.g., online advertising), the tendency to naively apply algorithms motivated by one domain, often online advertisements, can be called the "advertisement fallacy." Our main thesis is that law and public policy pose distinct methodological challenges that the machine learning community has not yet addressed. Machine learning will need to address these methodological problems to move "beyond ads." Public law, for instance, can pose multiple objectives, necessitate batched and delayed feedback, and require systems to learn rational, causal decision-making policies, each of which presents novel questions at the research frontier. We discuss a wide range of potential applications of sequential decision-making algorithms in regulation and governance, including public health, environmental protection, tax administration, occupational safety, and benefits adjudication. We use these examples to highlight research needed to render sequential decision making policy-compliant, adaptable, and effective in the public sector. We also note the potential risks of such deployments and describe how sequential decision systems can also facilitate the discovery of harms. We hope our work inspires more investigation of sequential decision making in law and public policy, which provide unique challenges for machine learning researchers with potential for significant social benefit.