A Machine Learning Theory Perspective on Strategic Litigation
This work addresses the problem of understanding strategic litigation's broader effects for legal theorists and policymakers, but it appears incremental as it applies existing ML theory concepts to a new domain without novel methodological breakthroughs.
The paper tackles the problem of modeling strategic litigation's impact on legal decision-making by framing it within a machine learning theory perspective, where a strategic litigator influences a learned decision rule from higher court rulings to affect future cases, but no concrete numerical results are provided.
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?