A Machine Learning Theory Perspective on Strategic Litigation

• URL: http://arxiv.org/abs/2506.03411v1
• Date: Tue, 03 Jun 2025 21:38:43 GMT
• Title: A Machine Learning Theory Perspective on Strategic Litigation
• Authors: Melissa Dutz, Han Shao, Avrim Blum, Aloni Cohen,
• Abstract summary: We consider a 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.<n>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.
• Score: 21.964841363939037
• License: http://creativecommons.org/licenses/by/4.0/
• 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?

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