Big Data Legal Scholarship: Toward a Research Program and Practitioner's Guide

Abstract

This Article seeks to take first steps toward developing a research program for big data legal scholarship by sketching its positive and normative components. The application of big data methods to the descriptive questions of law can generate broader consensus. This is because big data methods can provide greater comprehensiveness and less subjectivity than traditional approaches, and can diminish general disagreement over the categorization and theoretical development of law as a result. Positive application can increase the clarity of rules; uncover the relationship between judicial text and outcome; and comprehensively describe judicially-determined facts, the contents of legislation and regulation, or the contents of private agreements. Equipped with a normative framework, big data legal scholarship can lower the costs of judging, litigating, and administrating law; increase comparative justice and predictability; and support the advocacy of better rules and policies.

In addition to sketching theoretical foundations, this Article seeks to take first steps toward developing the core components of successful praxis. Handling and analyzing big data can be cumbersome, though the newcomer can avoid common pitfalls with care. Accordingly, there exist best practices for preprocessing, converting, and analyzing the data. Current analytical techniques germane to law include algorithmic classification, topic modeling, and large-dimension regression analysis.

First steps, by definition, are incomplete. The contours of big data legal scholarship and practice will undoubtedly shift over time to reflect new techniques and prevailing normative questions. This Article merely aspires to generate a conversation about how big data can enhance our understanding of law — what it is, and what it should be.

Keywords: big data, computational linguistics, law, legal scholarship