A Machine Learning Theory Perspective on Strategic Litigation
Abstract
Strategic litigation involves bringing a legal case to court with the goal of having a broader impact beyond resolving the case itself: for example, creating precedent which will influence future rulings. 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 learned decision rule, thereby affecting 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?
Keywords
Cite
@article{arxiv.2506.03411,
title = {A Machine Learning Theory Perspective on Strategic Litigation},
author = {Melissa Dutz and Han Shao and Avrim Blum and Aloni Cohen},
journal= {arXiv preprint arXiv:2506.03411},
year = {2025}
}