AI Litigation Outcome Prediction

Litigation analytics start with dockets. Federal cases are available through PACER, and vendors collect, clean and classify those records at scale. State court coverage is patchier because court systems and access rules differ. Products such as Lex Machina, which LexisNexis acquired in 2015, and the analytics in Westlaw and Bloomberg Law turn docket entries into measures. Examples include how often a judge grants summary judgment, typical time to trial, damages awarded in a type of case, and how a law firm has fared before a court. Most of what these tools report is descriptive: historical base rates. Outcome prediction goes further and uses those features to estimate the probability of a particular result. Academic work shows both promise and pitfalls. A 2017 model by Katz, Bommarito and Blackman predicted US Supreme Court decisions correctly about 70 percent of the time across many decades. A widely cited 2016 study of European Court of Human Rights cases reported high accuracy. Critics noted, however, that it used the court's own written summary of the facts, which is prepared after the outcome is known. Several limits apply to any prediction: Selection effects are large. Most cases settle, so decided cases are not a random sample. This point is associated with the Priest-Klein hypothesis; Samples shrink quickly. A judge may have ruled on only a handful of motions like yours; Outcome coding is messy. Someone has to classify partial grants and mixed rulings; and Law and personnel change over time. There are policy limits too. In 2019, France prohibited using judges' identity data to evaluate or predict their professional practices. The misconception to avoid is reading a base rate as your odds. A 40 percent grant rate describes past motions, not the strength of yours.