Mining the Harvard Caselaw Access Project

26 Pages Posted: 9 Mar 2020

See all articles by Felix Chang

Felix Chang

University of Cincinnati College of Law

Erin McCabe

affiliation not provided to SSRN

Zhaowei Ren

affiliation not provided to SSRN

Joshua Beckelhimer

affiliation not provided to SSRN

James Lee

affiliation not provided to SSRN

Date Written: January 31, 2020

Abstract

This Essay illustrates how machine learning can disrupt legal scholarship through the algorithmic extraction and analysis of big data. Specifically, we utilize data from Harvard Law School’s Caselaw Access Project to model how courts tackle two thorny question in antitrust: the measure of market power and the balance between antitrust and regulation.

Keywords: machine learning, AI, big data, data modeling, Harvard Law School Caselaw Access Project, data analytics, antitrust, market power, regulation

JEL Classification: K21,K49

Suggested Citation

Chang, Felix and McCabe, Erin and Ren, Zhaowei and Beckelhimer, Joshua and Lee, James, Mining the Harvard Caselaw Access Project (January 31, 2020). Available at SSRN: https://ssrn.com/abstract=3529257 or http://dx.doi.org/10.2139/ssrn.3529257

Felix Chang (Contact Author)

University of Cincinnati College of Law ( email )

P.O. Box 210040
Cincinnati, OH 45221-0040
United States

HOME PAGE: http://www.law.uc.edu/faculty-staff/felix-b-chang

Erin McCabe

affiliation not provided to SSRN

Zhaowei Ren

affiliation not provided to SSRN

Joshua Beckelhimer

affiliation not provided to SSRN

James Lee

affiliation not provided to SSRN

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
29
Abstract Views
137
PlumX Metrics