As national and regional governments form expert commissions to regulate “automated decision-making,” a new corporate-sponsored field of research proposes to formalize the elusive ideal of “fairness” as a mathematical property of algorithms and especially of their outputs. Computer scientists, economists, lawyers, lobbyists, and policy reformers wish to hammer out, in advance or in place of regulation, algorithmic redefinitions of “fairness” and such legal categories as “discrimination,” “disparate impact,” and “equal opportunity.”

But general aspirations to fair algorithms have a long history. This talk recounts some past attempts to answer questions of fairness through the use of algorithms. In particular, it focuses on “actuarial” practices of individualized risk classification in private insurance firms, consumer credit bureaus, and police departments since the late nineteenth century. The emerging debate on algorithmic fairness may be read as a response to the latest moral crisis of computationally managed racial capitalism.

Rodrigo Ochigame

History, Anthropology, & Science, Technology, and Society

MIT