Beyond Bias: Re-Imagining the Terms of ‘Ethical AI’ in Criminal Law

40 Pages Posted: 23 May 2019

Date Written: April 25, 2019

Abstract

Data-driven decision-making regimes, often branded as “artificial intelligence,” are rapidly proliferating across the US criminal justice system as a means of predicting and managing the risk of crime and addressing accusations of discriminatory practices. These data regimes have come under increased scrutiny, as critics point out the myriad ways that they can reproduce or even amplify pre-existing biases in the criminal justice system. This essay examines contemporary debates regarding the use of “artificial intelligence” as a vehicle for criminal justice reform, by closely examining two general approaches to, what has been widely branded as, “algorithmic fairness” in criminal law: 1) the development of formal fairness criteria and accuracy measures that illustrate the trade-offs of different algorithmic interventions and 2) the development of “best practices” and managerialist standards for maintaining a baseline of accuracy, transparency and validity in these systems. The essay argues that attempts to render AI-branded tools more accurate by addressing narrow notions of “bias,” miss the deeper methodological and epistemological issues regarding the fairness of these tools. The key question is whether predictive tools reflect and reinforce punitive practices that drive disparate outcomes, and how data regimes interact with the penal ideology to naturalize these practices. The article concludes by calling for an abolitionist understanding of the role and function of the carceral state, in order to fundamentally reformulate the questions we ask, the way we characterize existing data, and how we identify and fill gaps in existing data regimes of the carceral state.

Keywords: artificial intelligence, abolition, algorithm, policing, surveillance, governance, justice

Suggested Citation

Barabas, Chelsea, Beyond Bias: Re-Imagining the Terms of ‘Ethical AI’ in Criminal Law (April 25, 2019). Available at SSRN: https://ssrn.com/abstract=3377921 or http://dx.doi.org/10.2139/ssrn.3377921

Chelsea Barabas (Contact Author)

MIT Media Lab ( email )

77 Massachusetts Avenue
Cambridge, MA 02139
United States

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