Artificial Intelligence and Machine Learning can help generates results for litigation
In 2004, a group of professors from Washington University tested their algorithm’s accuracy in forecasting Supreme Court decisions on all 628 argued cases in 2002. They compared their algorithm’s results against a team of experts’ findings. The statistical model by the researchers proved to be a better predictor by correctly forecasting 75 percent of the outcomes compared to the expert’s 59 percent accuracy. Based on this study, there is compelling evidence that AI should complement manual review.
Currently, the company called Intraspexion has patented software systems can search for “high-risk documents and displays them according to the level of risk that the AI has determined.” When a user clicks on a document, risk terms as identified by subject matter experts through the algorithm are highlighted. According to the company, users can choose which documents put them at risk for litigation when they use the software. Another tool, Ravel Law, is said to be able to identify outcomes based on relevant case law, judge rulings and referenced language from more than 400 courts. The product’s “Judge Dashboard feature contains cases, citations, circuits and decisions of a specific judge that is said to aid lawyers in understanding how judge is likely to rule on a case.” The use of these technologies can help law firms decide high priority cases and how to best manage resources for each case, given the likelihood of success for each one. Given that lawyers often focus on possible outcomes, AI and Machine learning tools can help lawyers better integrate probability into their decision-making.