Friday, October 18, 2019
Crystal Yang and Will Dobbie (Harvard Law School and Harvard University - Harvard Kennedy School (HKS)) have posted Equal Protection Under Algorithms: A New Statistical and Legal Framework on SSRN. Here is the abstract:
In this paper, we provide a new statistical and legal framework to understand the legality and fairness of predictive algorithms under the Equal Protection Clause. We begin by reviewing the main legal concerns regarding the use of protected characteristics such as race and the correlates of protected characteristics such as criminal history. The use of race and non-race correlates in predictive algorithms generates direct and proxy effects of race, respectively, that can lead to racial disparities that many view as unwarranted and discriminatory. These effects have led to the mainstream legal consensus that the use of race and non-race correlates in predictive algorithms is both problematic and potentially unconstitutional under the Equal Protection Clause. This mainstream position is also reflected in practice, with all commonly-used predictive algorithms excluding race and many excluding non-race correlates such as employment and education.
In the second part of the paper, we challenge the mainstream legal position that the use of a protected characteristic always violates the Equal Protection Clause.
We conclude by empirically testing our proposed algorithms in the context of the New York City pretrial system. We show that nearly all commonly-used algorithms violate the spirit of the Equal Protection Clause by including variables that are correlated with race, generating substantial proxy effects that unfairly disadvantage blacks relative to whites. Both of our proposed algorithms substantially reduce the number of black defendants detained compared to these commonly-used algorithms by eliminating these proxy effects. These findings suggest a fundamental rethinking of the Equal Protection doctrine as it applies to predictive algorithms and the folly of relying on commonly-used algorithms.