Does Lawyering Matter? Predicting Judicial Decisions from Legal Briefs, and What That Means for Access to Justice
45 Pages Posted: 26 Mar 2021 Last revised: 18 Oct 2021
Date Written: March 24, 2021
This study uses linguistic analysis and machine learning techniques to predict summary judgment outcomes from the text of the parties’ briefs. We test the predictive power of textual characteristics, stylistic features, and citation usage, and find that citations to precedent – their frequency, their patterns, and their popularity in other briefs – are the most predictive of a summary judgment win. This suggests that good lawyering may boil down to good legal research. However, good legal research is expensive, and the primacy of citations in our models raises concerns about access to justice. Here, our citation-based models also suggest promising solutions. We propose a freely available, computationally-enabled citation identification and brief bank tool, which would extend to all litigants the benefits of good lawyering and open up access to justice.
Keywords: machine learning, access to justice, text analytics, summary judgment, employment law, employment discrimination
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