Predicting and Understanding Law-Making with Machine Learning

Nay JJ (2017) Predicting and understanding law-making with word vectors and an ensemble model. PLoS ONE 12(5): e0176999

12 Pages Posted: 20 Jul 2016 Last revised: 21 Sep 2017

John Nay

New York University School of Law; Harvard University - Berkman Klein Center for Internet & Society

Date Written: July 6, 2016

Abstract

Out of nearly 70,000 bills introduced in the U.S. Congress from 2001 to 2015, only 2,513 were enacted. We developed a machine learning approach to forecasting the probability that any bill will become law. Starting in 2001 with the 107th Congress, we trained models on data from previous Congresses, predicted all bills in the current Congress, and repeated until the 113th Congress served as the test. For prediction we scored each sentence of a bill with a language model that embeds legislative vocabulary into a semantic-laden vector space. This language representation enables our investigation into which words increase the probability of enactment for any topic. To test the relative importance of text and context, we compared the text model to a context-only model that uses variables such as whether the bill’s sponsor is in the majority party. To test the effect of changes to bills after their introduction on our ability to predict their final outcome, we compared using the bill text and meta-data available at the time of introduction with using the most recent data. At the time of introduction context-only predictions outperform text-only, and with the newest data text-only outperforms context-only. Combining text and context always performs best. We conducted a global sensitivity analysis on the combined model to determine important factors predicting enactment.

Keywords: Forecasting, Ensemble Modeling, Natural Language Processing, Congress, Law

Suggested Citation

Nay, John, Predicting and Understanding Law-Making with Machine Learning (July 6, 2016). Nay JJ (2017) Predicting and understanding law-making with word vectors and an ensemble model. PLoS ONE 12(5): e0176999. Available at SSRN: https://ssrn.com/abstract=2808448

John Nay (Contact Author)

New York University School of Law

40 Washington Square South
New York, NY 10012-1099
United States

Harvard University - Berkman Klein Center for Internet & Society ( email )

Harvard Law School
23 Everett, 2nd Floor
Cambridge, MA 02138
United States

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