The Scored Society: Due Process for Automated Predictions

34 Pages Posted: 8 Jan 2014 Last revised: 23 Apr 2014

See all articles by Danielle Keats Citron

Danielle Keats Citron

University of Virginia School of Law

Frank Pasquale

Cornell Law School; Cornell Tech

Date Written: 2014


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.

Keywords: Big Data, predictions, artificial intelligence

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:

Danielle Keats Citron (Contact Author)

University of Virginia School of Law ( email )

580 Massie Road
Charlottesville, VA 22903
United States

Frank A. Pasquale

Cornell Law School ( email )

Myron Taylor Hall
Ithaca, NY 14853

Cornell Tech ( email )

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

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