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A Machine Learning Theory Perspective on Strategic Litigation

Authors Melissa Dutz , Han Shao , Avrim Blum , Aloni Cohen

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Subject Classification

ACM Subject Classification
  • Theory of computation → Machine learning theory
  • Theory of computation → Algorithmic game theory
Keywords
  • Strategic Litigation
  • Machine Learning Theory
  • Law

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Abstract

Strategic litigation involves bringing a case to court with the goal of having an impact beyond resolving the particular dispute at hand. In a common law system, one way a case may have far-reaching impact is by establishing new legal precedent that later courts must follow. In this paper, we explore strategic litigation from the perspective of machine learning theory. We consider an abstract model of a common law legal system where a lower court decides new cases by applying a decision rule learned from a higher court’s past rulings. In this model, we explore the power of a strategic litigator, who strategically brings cases to the higher court to influence the decision rule applied by the lower court in future cases. We explore questions including: What impact can a strategic litigator have? Which cases should a strategic litigator bring to court? Does it ever make sense for a strategic litigator to bring a case when they are sure the court will rule against them? We show that this strategic case selection problem has interesting structure, with even simple settings exhibiting counterintuitive phenomena. When cases are represented by points in one dimension and the lower court’s learning algorithm is nearest neighbor, or as points in d dimensions and the lower court’s learning algorithm is a support vector machine, we characterize the set of inducible decision rules and develop algorithms for selecting an optimal set of cases to bring to the higher court given the strategic litigator’s objectives.

Cite As Get BibTex

Melissa Dutz, Han Shao, Avrim Blum, and Aloni Cohen. A Machine Learning Theory Perspective on Strategic Litigation. In 7th Symposium on Foundations of Responsible Computing (FORC 2026). Leibniz International Proceedings in Informatics (LIPIcs), Volume 368, pp. 19:1-19:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.FORC.2026.19

Author Details

Melissa Dutz
  • Toyota Technological Institute at Chicago, IL, USA
Han Shao
  • University of Maryland, College Park, MD, USA
Avrim Blum
  • Toyota Technological Institute at Chicago, IL, USA
Aloni Cohen
  • The University of Chicago, IL, USA

Funding

  • Dutz, Melissa: Melissa Dutz was supported in part by an NSF CSGrad4US fellowship under Grant No. 2313998. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.
  • Shao, Han: Han Shao was supported in part by Harvard CMSA.
  • Blum, Avrim: Avrim Blum was supported in part by the National Science Foundation under grant CCF-2212968, and by ECCS-2216899. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the U.S. National Science Foundation.

References

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