Abstract

http://ssrn.com/abstract=2376209
 


 



The Scored Society: Due Process for Automated Predictions


Danielle Keats Citron


University of Maryland Francis King Carey School of Law; Yale University - Yale Information Society Project; Stanford Law School Center for Internet and Society

Frank A. Pasquale III


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

2014

Washington Law Review, Vol. 89, 2014, p. 1-
U of Maryland Legal Studies Research Paper No. 2014-8

Abstract:     
Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess whether we are good credit risks, desirable employees, reliable tenants, valuable customers — or deadbeats, shirkers, menaces, and “wastes of time.” Crucial opportunities are on the line, including the ability to obtain loans, work, housing, and insurance. Though automated scoring is pervasive and consequential, it is also opaque and lacking oversight. In one area where regulation does prevail — credit — the law focuses on credit history, not the derivation of scores from data.

Procedural regularity is essential for those stigmatized by “artificially intelligent” scoring systems. The American due process tradition should inform basic safeguards. Regulators should be able to test scoring systems to ensure their fairness and accuracy. Individuals should be granted meaningful opportunities to challenge adverse decisions based on scores miscategorizing them. Without such protections in place, systems could launder biased and arbitrary data into powerfully stigmatizing scores.

Number of Pages in PDF File: 34

Keywords: Big Data, predictions, artificial intelligence

Accepted Paper Series





Download This Paper

Date posted: January 8, 2014 ; Last revised: April 23, 2014

Suggested Citation

Citron, Danielle Keats and Pasquale, Frank A., The Scored Society: Due Process for Automated Predictions (2014). Washington Law Review, Vol. 89, 2014, p. 1-; U of Maryland Legal Studies Research Paper No. 2014-8. Available at SSRN: http://ssrn.com/abstract=2376209

Contact Information

Danielle Keats Citron (Contact Author)
University of Maryland Francis King Carey School of Law ( email )
500 West Baltimore Street
Baltimore, MD 21201-1786
United States
Yale University - Yale Information Society Project
127 Wall Street
New Haven, CT 06511
United States
Stanford Law School Center for Internet and Society
Palo Alto, CA
United States
Frank A. Pasquale III
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
Feedback to SSRN


Paper statistics
Abstract Views: 2,434
Downloads: 301
Download Rank: 58,607

© 2014 Social Science Electronic Publishing, Inc. All Rights Reserved.  FAQ   Terms of Use   Privacy Policy   Copyright   Contact Us
This page was processed by apollo5 in 0.579 seconds