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Prediction, Persuasion, and the Jurisprudence of Behaviorism

19 Pages Posted: 10 Nov 2017  

Frank A. Pasquale III

University of Maryland Francis King Carey School of Law; Yale University - Yale Information Society Project

Glyn Cashwell

Vistronix

Date Written: November 8, 2017

Abstract

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: https://ssrn.com/abstract=3067737

Frank Pasquale (Contact Author)

University of Maryland Francis King Carey School of Law ( email )

500 West Baltimore Street
Baltimore, MD 21201-1786
United States
410-706-4820 (Phone)
410-706-0407 (Fax)

Yale University - Yale Information Society Project ( email )

127 Wall Street
New Haven, CT 06511
United States

Glyn Cashwell

Vistronix ( email )

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

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