Artificial Intelligence and Law 30 (1):1-17 (2022)

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Duncan Purves
University of Florida
Abstract
Machine Learning has become a popular tool in a variety of applications in criminal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbating social injustices. However, there is little academic research on the importance of fairness in machine learning applications in policing. Although prior research has shown that machine learning models can handle some tasks efficiently, they are susceptible to replicating systemic bias of previous human decision-makers. While there is much research on fair machine learning in general, there is a need to investigate fair machine learning techniques as they pertain to the predictive policing. Therefore, we evaluate the existing publications in the field of fairness in machine learning and predictive policing to arrive at a set of standards for fair predictive policing. We also review the evaluations of ML applications in the area of criminal justice and potential techniques to improve these technologies going forward. We urge that the growing literature on fairness in ML be brought into conversation with the legal and social science concerns being raised about predictive policing. Lastly, in any area, including predictive policing, the pros and cons of the technology need to be evaluated holistically to determine whether and how the technology should be used in policing.
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DOI 10.1007/s10506-021-09286-4
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Machine Decisions and Human Consequences.Teresa Scantamburlo, Andrew Charlesworth & Nello Cristianini - 2019 - In Karen Yeung & Martin Lodge (eds.), Algorithmic Regulation. Oxford: Oxford University Press.
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A Moral Framework for Understanding of Fair ML Through Economic Models of Equality of Opportunity.Hoda Heidari - 2019 - Proceedings of the Conference on Fairness, Accountability, and Transparency 1.

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