Jon Kleinberg, Jens Ludwig, Sendhil Mullainathan & Cass R. Sunstein, Algorithms as Discrimination Detectors, 117 Proc. Nat'l Acad. Sci. 33096 (Dec. 1, 2020).
Abstract: This paper results from the Arthur M. Sackler Colloquium of the National Academy of Sciences, “The Science of Deep Learning,” held March 13–14, 2019, at the National Academy of Sciences in Washington, DC. Preventing discrimination requires that we have means of detecting it, and this can be enormously difficult when human beings are making the underlying decisions. As applied today, algorithms can increase the risk of discrimination. But as we argue here, algorithms by their nature require a far greater level of specificity than is usually possible with human decision making, and this specificity makes it possible to probe aspects of the decision in additional ways. With the right changes to legal and regulatory systems, algorithms can thus potentially make it easier to detect—and hence to help prevent—discrimination.