Download the paper here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=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
January 7, 2014
Washington Law Review, Vol. 89, 2014
U of Maryland Legal Studies Research Paper No. 2014-8
Abstract: Big Data is increasingly mined to rank and rate individuals. Predictive algorithms assess individuals as good credit risks, desirable employees, reliable tenants, and valuable customers. People’s crucial life opportunities are on the line, including their ability to obtain loans, work, housing, and insurance. As pervasive and consenquential as automated scoring is, so is its secrecy and lack of oversight. Procedural regularity is essential for those stigmatized by “artificially intelligent” systems. Lessons from our due process tradition can provide basic safeguards for our scoring society. 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.