Tag: Algorithms

Big Data and Discrimination

Read Paper

This essay discusses the legal requirements of pricing credit and the architecture of machine learning and intelligent algorithms to provide an overview of legislative gaps, legal solutions, and a framework for testing discrimination that evaluates algorithmic pricing rules. Using real-world mortgage data, the authors find that restricting the data characteristics within the algorithm can increase pricing gaps while having a limited impact on disparity.

Talia B. Gillis & Jann L. Spiess, University of Chicago Law Review

Big Data’s Disparate Impact

Read Paper

This paper examines concerns about big data’s disparate impact risk from the perspective of American antidiscrimination law, more specifically, through Title VII’s prohibition of discrimination in employment. The paper also calls out the legal and political difficulties of addressing and remedying this type of discrimination, in particular, the tension between the two major theories underlying antidiscrimination law: anticlassification and antisubordination.

104 California Law Review 671 (2016)

Solon Barocas, Andrew D. Selbst

Accountability of AI Under the Law: The Role of Explanation

Read Paper

This paper examines AI systems and how these systems should be held accountable, in particular by one method: explanation. The paper focuses on using explanation from AI systems at the right time to improve accountability, and reviews societal, moral, and legal norms around explanation. The paper ends with advocating that at present, AI systems can and should be held responsible to a similar standard of explanation as humans are, and adapt as the future changes.

Berkman Center Research Publication Forthcoming; Harvard Public Law Working Paper No. 18-07

Finale Doshi-Velez, Mason Kortz, Ryan Budish, Christopher Bavitz, Samuel J. Gershman, David O’Brien, Stuart Shieber, Jim Waldo, David Weinberger, Alexandra Wood.

Join our Newsletter

Translate »