Law Quarterly

Classifying Legal Approaches: A Machine Learning Project


There is no denying the significant impact technology has had on our society in the past two decades. Everything is moving at an increasingly faster pace than it had in the past, and the legal field, known for being bound by norms, tradition, and precedence has struggled to keep up. Today, a new paradigm is emerging. The availability of massive volumes of data and computational power is expanding the possibilities of legal “text-mining”, machine learning, and AI. In this article we explore some of these methods to help us classify the legal approaches made in the decisions of a set of court cases, evaluate the results, and discuss future outlook.

Precedents is the defining feature of the common law system, attaining special legal significance in virtue of its practical, and not merely theoretical, authority over the content of the law. To measure the practical effects of precedents, we have to first categorize the different types of approaches and interpretations that judges take. The set of cases that we will analyze comes from all trial, district court cases since 2014 that have cited Iqbal v. Ashcroft (2009). Past studies compared decisions in the period before and after Iqbal to find that district courts were not applying standards as handed down by upper courts and that motions to dismiss had increased after Iqbal. While they manually counted and labeled the number of cases that falls under each category, our project aims to automate this process with the help of machine learning driven by complex statistical techniques. (more…)