When Is an Algorithm Transparent?: Predictive Analytics, Privacy, and Public Policy

IEEE: Security & Privacy, May/June 2018

9 Pages Posted: 12 Oct 2017 Last revised: 21 Sep 2018

See all articles by Robert H. Sloan

Robert H. Sloan

University of Illinois at Chicago

Richard Warner

Chicago-Kent College of Law

Date Written: October 12, 2017

Abstract

The rise of data mining and predictive analytics makes the problem of algorithmic transparency pressing. Solving that problem requires answers to two questions. What are the criteria of transparency? And how do you tell whether a predictive system meets those criteria? We confine our attention to consumers engaged in commercial transactions because this already raises most of the questions that concern us. We propose that predictive systems are transparent for consumers if they able to readily ascertain the risks and benefits associated with the predictive systems to which they are subject. We examine three ways to meet this condition: disclosing source code; techniques that reveal how an algorithm works without disclosing source code; and reliance on informational norms.

Keywords: transparency, algorithms, transparency of algorithms, privacy, public policy, predictive analytics, norms, informational norms

JEL Classification: C80, K29

Suggested Citation

Sloan, Robert H. and Warner, Richard, When Is an Algorithm Transparent?: Predictive Analytics, Privacy, and Public Policy (October 12, 2017). IEEE: Security & Privacy, May/June 2018. Available at SSRN: https://ssrn.com/abstract=3051588 or http://dx.doi.org/10.2139/ssrn.3051588

Robert H. Sloan

University of Illinois at Chicago ( email )

1200 W Harrison St
Chicago, IL 60607
United States

Richard Warner (Contact Author)

Chicago-Kent College of Law ( email )

565 West Adams St.
Chicago, IL 60661
United States

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