Prediction, Persuasion, and the Jurisprudence of Behaviorism

19 Pages Posted: 10 Nov 2017

See all articles by Frank Pasquale

Frank Pasquale

Cornell Law School; Cornell Tech

Glyn Cashwell


Date Written: November 8, 2017


Machine learning experts are feeding judicial opinions to algorithms, to predict how future cases will be decided. We call the use of such predictive analytics in judicial contexts a jurisprudence of behaviorism, as it rests on a fundamentally Skinnerian model of cognition as a black boxed transformation of inputs into outputs. In this model, persuasion is passé; what matters is prediction. After describing and critiquing a recent study that has advanced this jurisprudence of behaviorism, we question the value of such research.

Keywords: Black Box Algorithms, Judicial Decisions, Predictive Analytics, Natural Language Processing, Machine Learning, Legal Realism, N-Grams, Spurious Correlations

Suggested Citation

Pasquale, Frank A. and Cashwell, Glyn, Prediction, Persuasion, and the Jurisprudence of Behaviorism (November 8, 2017). U of Maryland Legal Studies Research Paper No. 2017-34, Available at SSRN:

Frank A. Pasquale (Contact Author)

Cornell Law School ( email )

Myron Taylor Hall
Ithaca, NY 14853

Cornell Tech ( email )

111 8th Avenue #302
New York, NY 10011
United States

Glyn Cashwell

Vistronix ( email )

11091 Sunset Hills Rd
Suite 700
Reston, VA 20190
United States

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics