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This paper synthesizes scholarship from several academic disciplines to identify and analyze five major ethical challenges facing data-driven policing. Because the term “data-driven policing” encompasses a broad swath of technologies, we first outline several data-driven policing initiatives currently in use in the United States. We then lay out the five ethical challenges. Certain of these challenges have received considerable attention already, while others have been largely overlooked. In many cases, the challenges have been articulated in the context of related discussions, but their distinctively ethical dimensions have not been explored in much detail. Our goal here is to articulate and clarify these ethical challenges, while also highlighting areas where these issues intersect and overlap. Ultimately, responsible data-driven policing requires collaboration between communities, academics, technology developers, police departments, and policy makers to confront and address these challenges. And as we will see, it may also require critically reexamining the role and value of police in society. 

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. 

Effective advisement can help to address the disproportionately lower self-efficacy, identity, and sense of belonging experienced by Black students in computing degree programs.. Black social media influencers who produce video log (vlog) commentary content on the YouTube platform were investigated to determine the influence they have on improving computing identity for Black students. This exploratory study consists of three studies: (1) a synthesis of vlog commentary college and career advisement videos, coding for the quality of advisement, usability, and user experience; (2) an advisor effectiveness and user experience survey using a selected Black social media influencer who provides computing college and career advisement; and (3) a user experience and interaction preference survey using a selected Black social media influencer. Findings suggest YouTube influencers could be effective, particularly for beginners in the computing field. Future studies intend to further explore Black computing advisement through social media over a long term and at varying levels of interaction.