  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.
