As legal scholars begin to digest AI's ramifications for law and legal analysis, a recent paper exploits the Supreme Court Database to illustrate AI's ability topredict case outcomes. In A general approach for predicting the behavior of the Supreme Court of the United States, Daniel Katz (Chicago-Kent) et al. explore almost two centuries of Supreme Court decisions and use an algorithm to successfully predict 70.2% of the case outcomes and 71.9% of individual justice-level votes. The abstract follows.
"Building on developments in machine learning and prior work in the science of judicial prediction, we construct a model designed to predict the behavior of the Supreme Court of the United States in a generalized, out-of-sample context. To do so, we develop a time-evolving random forest classifier that leverages unique feature engineering to predict more than 240,000 justice votes and 28,000 cases outcomes over nearly two centuries (1816-2015). Using only data available prior to decision, our model outperforms null (baseline) models at both the justice and case level under both parametric and non-parametric tests. Over nearly two centuries, we achieve 70.2% accuracy at the case outcome level and 71.9% at the justice vote level. More recently, over the past century, we outperform an in-sample optimized null model by nearly 5%. Our performance is consistent with, and improves on the general level of prediction demonstrated by prior work; however, our model is distinctive because it can be applied out-of-sample to the entire past and future of the Court, not a single term. Our results represent an important advance for the science of quantitative legal prediction and portend a range of other potential applications."