A Machine Learning Theory Perspective on Strategic Litigation
원문 초록
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?
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Self-Supervised Evolution Operator Learning for High-Dimensional Dynamical Systems
auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory
Double descent in quantum machine learning
Gradient Equilibrium in Online Learning: Theory and Applications
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
토론
이 논문과 연결된 포럼 글이 아직 없습니다.