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30 A.I. & L. 1 (2022)

handle is hein.journals/artinl30 and id is 1 raw text is: Artificial Intelligence and Law (2022) 30:1-17 https://doi.org/1 0.1007/si 0506-021-09286-4 REVIEW ARTICLE A review of predictive policing from the perspective of fairness Kiana Alikhademi' - Emma Drobina - Diandra Prioleau1. Brianna Richardson' Duncan Purves2 - Juan E. Gilbert' Accepted: 31 March 2021 / Published online: 15 April 2021 ©The Author(s), under exclusive licence to Springer Nature B.V. 2021 Abstract Machine Learning has become a popular tool in a variety of applications in crimi- nal justice, including sentencing and policing. Media has brought attention to the possibility of predictive policing systems causing disparate impacts and exacerbat- ing 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 inves- tigate 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. Keywords Fairness - Algorithmic fairness - Predictive policing - Al in criminal justice E Kiana Alikhademi kalikhademi@ufl.edu Extended author information available on the last page of the article I_) Springer

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