论文标题
关于负面先例在法律结果预测中的作用
On the Role of Negative Precedent in Legal Outcome Prediction
论文作者
论文摘要
每个法律案件都以以下两种方式之一制定法律。它要么扩大其范围，在这种情况下，它设定了积极的先例，或者缩小了范围，在这种情况下，它设定了负面的先例。法律结果预测是对积极结果的预测，是AI中日益流行的任务。相比之下，我们将重点转移到这里的负面结果，并引入了负面结果预测的新任务。我们发现了现有模型预测积极和负面结果的能力中的不对称性。如果我们使用的最新结果预测模型可以预测75.06 F1的积极结果，则它仅在10.09 F1的情况下预测负结果，比随机基线更糟。为了解决这个绩效差距，我们开发了两个新模型，该模型灵感来自法院流程的动态。我们的第一个模型将积极的预测评分显着提高到77.15 F1，而第二个模型将负结果预测性能翻了一番，达到24.01 F1。尽管有这种改进，但转移到负面结果的重点表明，对于结果预测模型仍有很大的改进空间。
Every legal case sets a precedent by developing the law in one of the following two ways. It either expands its scope, in which case it sets positive precedent, or it narrows it, in which case it sets negative precedent. Legal outcome prediction, the prediction of positive outcome, is an increasingly popular task in AI. In contrast, we turn our focus to negative outcomes here, and introduce a new task of negative outcome prediction. We discover an asymmetry in existing models' ability to predict positive and negative outcomes. Where the state-of-the-art outcome prediction model we used predicts positive outcomes at 75.06 F1, it predicts negative outcomes at only 10.09 F1, worse than a random baseline. To address this performance gap, we develop two new models inspired by the dynamics of a court process. Our first model significantly improves positive outcome prediction score to 77.15 F1 and our second model more than doubles the negative outcome prediction performance to 24.01 F1. Despite this improvement, shifting focus to negative outcomes reveals that there is still much room for improvement for outcome prediction models.
