Journal Article | March 2022

Fairness in Algorithmic Policing

This paper argues that the prevailing focus on racial bias has overshadowed two normative factors that are essential to a full assessment of the moral permissibility of predictive policing: fairness in the social distribution of the benefits and burdens of policing as well as the distinctive role of consent in determining fair distribution. When these normative factors are given their due attention, several requirements emerge for the fair implementation of predictive policing. Among these requirements are that police departments inform and solicit buy-in from affected communities about strategic decision-making and that departments favor non-enforcement-oriented interventions.

Journal Article | January 2022

Five ethical challenges facing data-driven policing

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.

Literature review | April 2021

A review of predictive policing from the perspective of fairness

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.

Report | September 2020

Artificial Intelligence Ethics and Predictive Policing: A Roadmap for Research

Against a backdrop of historic unrest and criticism, the institution of policing is at an inflection point. Policing practices, and the police use of technology, are under heightened scrutiny. One of the most prominent and controversial of these practices centrally involves technology and is often called "predictive policing." Predictive policing is the use of computer algorithms to forecast when and where crimes will take place — and sometimes even to predict the identities of perpetrators or victims. Criticisms of predictive policing combine worries about artificial intelligence and bias, about power structures and democratic accountability, about the responsibilities of private tech companies selling the software, and about the fundamental relationship between state and citizen. In this report, we present the initial findings from a three-year project to investigate the ethical implications of predictive policing and develop ethically-sensitive and empirically-informed best practices for both those developing these technologies and the police departments using them.

Article | June 2, 2020

Winning the Battle, Losing the War

At least since the Industrial Revolution, humanity has had a troubled relationship with technology. Even while standards of living have skyrocketed and life expectancies lengthened, we have often been shocked or dismayed by the unforeseen disruptions that technology brings with it. I suspect that a major source of this myopia is the naivete of one popular view of technology: that technologies are merely neutral tools and that our engagements with particular technologies are episodic or, in the words of Langdon Winner, brief, voluntary, and unproblematic. I think view is simplistic – and appreciating both the consequences of longer-term technological policies and the interplay between a technology and its social context can help us anticipate these negative consequences. In particular, we should appreciate how even an efficient and reliable technology can nurture social circumstances that will undermine the very goals that technology is meant to serve.

Syllabus | Coming soon

Police Ethics and Police Technology

The first professional police force was founded in Boston, Massachusetts in 1838. Since that time police have long played a central role in the promotion of safety and security in the United States. But a growing chorus of critics is questioning the place of policing in maintaining social order. Among the concerns being voiced are that police work increasingly violates civil liberties, that policing is racially biased in ways that oppress marginalized people, and that the scope of police work outstrips their expertise. Emerging policing technologies can either ameliorate or aggravate these concerns. In this course we will investigate, through the methods of moral philosophy, the moral foundations of policing, some recent ethical controversies about the role and conduct of police in society, and the appropriate role of technology in policing. Topics include an introduction to ethical issues in artificial intelligence, the role of police in society, institutional critiques of policing and big data technology, police discretion, predictive policing, surveillance and data collection, non-lethal weapons and police use of force, and future directions in policing and policing technology.

Copyright Cal Poly 2020

Ryan Jenkins

Associate Professor

Duncan Purves

Associate Professor