The American justice system, from police departments to the courts, is increasingly turning to information technology for help identifying potential offenders, determining where, geographically, to allocate enforcement resources, assessing flight risk and the potential for recidivism amongst arrestees, and making other judgments about when, where, and how to manage crime. In particular, there is a focus on machine learning and other data analytics tools, which promise to accurately predict where crime will occur and who will perpetrate it. Activists and academics (...) have begun to raise critical questions about the use of these tools in policing contexts. In this chapter, I review the emerging critical literature on predictive policing and contribute to it by raising ethical questions about the use of predictive analytics tools to identify potential offenders. Drawing from work on the ethics of profiling, I argue that the much-lauded move from reactive to preemptive policing can mean wrongfully generalizing about individuals, making harmful assumptions about them, instrumentalizing them, and failing to respect them as full ethical persons. I suggest that these problems stem both from the nature of predictive policing tools and from the sociotechnical contexts in which they are implemented... (shrink)

According to the medical world, hysteria is a thing of the past, an outdated diagnosis that has disappeared for good. This book argues that hysteria is in fact alive and well. Hyperventilating, we rush from one incident into the next - there is hardly time for a breather. From the worldwide run on toilet paper to cope with coronavirus fears to the overheated discussions about immigration and overwrought reactions to the levels of crime and disorder around us, we live in (...) a culture of hysteria. While hysteria is typically discussed in emotional terms - as an obstacle to be overcome - it nevertheless has very real consequences in everyday life. Irritating though this may be, hysteria needs to be taken seriously, for what it tells us about our society and way of life. That is why Marc Schuilenburg examines what hysteria is and why it is fuelled by a culture that not only abuses, but also encourages and rewards it. Written in a clear and direct style, this book will appeal to students and scholars of sociology, criminology, philosophy and all those interested in hysteria and how it permeates late modern society. (shrink)

Sally Haslanger has recently argued that philosophical focus on implicit bias is overly individualist, since social inequalities are best explained in terms of social structures rather than the actions and attitudes of individuals. I argue that questions of individual responsibility and implicit bias, properly understood, do constitute an important part of addressing structural injustice, and I propose an alternative conception of social structure according to which implicit biases are themselves best understood as a special type of structure.

In light of the recent emergence of predictive techniques in law enforcement to forecast crimes before they occur, this paper examines the temporal operation of power exercised by predictive policing algorithms. I argue that predictive policing exercises power through a paranoid style that constitutes a form of temporal governmentality. Temporality is especially pertinent to understanding what is ethically at stake in predictive policing as it is continuous with a historical racialized practice of organizing, managing, controlling, and stealing time. After first (...) clarifying the concept of temporal governmentality, I apply this lens to Chicago Police Department’s Strategic Subject List. This predictive algorithm operates, I argue, through a paranoid logic that aims to preempt future possibilities of crime on the basis of a criminal past codified in historical crime data. (shrink)

The debate about the ethical implications of Artificial Intelligence dates from the 1960s :741–742, 1960; Wiener in Cybernetics: or control and communication in the animal and the machine, MIT Press, New York, 1961). However, in recent years symbolic AI has been complemented and sometimes replaced by Neural Networks and Machine Learning techniques. This has vastly increased its potential utility and impact on society, with the consequence that the ethical debate has gone mainstream. Such a debate has primarily focused on principles—the (...) ‘what’ of AI ethics —rather than on practices, the ‘how.’ Awareness of the potential issues is increasing at a fast rate, but the AI community’s ability to take action to mitigate the associated risks is still at its infancy. Our intention in presenting this research is to contribute to closing the gap between principles and practices by constructing a typology that may help practically-minded developers apply ethics at each stage of the Machine Learning development pipeline, and to signal to researchers where further work is needed. The focus is exclusively on Machine Learning, but it is hoped that the results of this research may be easily applicable to other branches of AI. The article outlines the research method for creating this typology, the initial findings, and provides a summary of future research needs. (shrink)

Is our case strong enough to go to trial? Will interest rates go up? Can I trust this person? Such questions - and the judgments required to answer them - are woven into the fabric of everyday experience. This book, first published in 2002, examines how people make such judgments. The study of human judgment was transformed in the 1970s, when Kahneman and Tversky introduced their 'heuristics and biases' approach and challenged the dominance of strictly rational models. Their work highlighted (...) the reflexive mental operations used to make complex problems manageable and illuminated how the same processes can lead to both accurate and dangerously flawed judgments. The heuristics and biases framework generated a torrent of influential research in psychology - research that reverberated widely and affected scholarship in economics, law, medicine, management, and political science. This book compiles the most influential research in the heuristics and biases tradition since the initial collection of 1982. (shrink)

This paper focuses on the potential of “equitech”—AI technology that improves equity. Recently, interventions have been developed to reduce the harm of implicit bias, the automatic form of stereotype or prejudice that contributes to injustice. However, these interventions—some of which are assisted by AI-related technology—have significant limitations, including unintended negative consequences and general inefficacy. To overcome these limitations, we propose a two-dimensional framework to assess current AI-assisted interventions and explore promising new ones. We begin by using the case of human (...) resource recruitment as a focal point to show that existing approaches have exploited only a subset of the available solution space. We then demonstrate how our framework facilitates the discovery of new approaches. The first dimension of this framework helps us systematically consider the analytic information, intervention implementation, and modes of human-machine interaction made available by advancements in AI-related technology. The second dimension enables the identification and incorporation of insights from recent research on implicit bias intervention. We argue that a design strategy that combines complementary interventions can further enhance the effectiveness of interventions by targeting the various interacting cognitive systems that underlie implicit bias. We end with a discussion of how our cognitive interventions framework can have positive downstream effects for structural problems. (shrink)

Should you be targeted by police for a crime that AI predicts you will commit? In this paper, we analyse when, and to what extent, the person-based predictive policing (PP) — using AI technology to identify and handle individuals who are likely to breach the law — could be justifiably employed. We first examine PP’s epistemological limits, and then argue that these defects by no means refrain from its usage; they are worse in humans. Next, based on major AI ethics (...) guidelines (IEEE, EU, and RIKEN, etc.), we refine three basic moral principles specific to person-based PP. We also derive further requirements from case studies, including debates in Chicago, New Orleans, San Francisco, Tokyo, and cities in China. Instead of rejecting PP programs, we analyse what necessary conditions should be met for using the tool to achieve social good. While acknowledging its risks, we conclude that the person-based PP could be beneficial in community policing, especially when merging into a larger governance framework of the social safety net. (shrink)

This paper presents a novel framework for evaluating racial profiling, including 'rational profiling' that does in fact decrease crime rates. It argues that while profiling some groups, such as African Americans and Muslims, is impermissible, profiling others, such as white men, may be permissible. The historical and sociological context matters significantly. Along the way, the paper develops a new theory of what expressive harms are, why they matter, and when it is the responsibility of the state to correct them.

Recent work on social injustice has focused on implicit bias as an important factor in explaining persistent injustice in spite of achievements on civil rights. In this paper, I argue that because of its individualism, implicit bias explanation, taken alone, is inadequate to explain ongoing injustice; and, more importantly, it fails to call attention to what is morally at stake. An adequate account of how implicit bias functions must situate it within a broader theory of social structures and structural injustice; (...) changing structures is often a precondition for changing patterns of thought and action and is certainly required for durable change. (shrink)

Standpoint epistemologies are based on two central theses: the situated knowledge thesis and the thesis of epistemic privilege. The bias paradox suggests that there is a tension between these two notions, in the sense that they are self-contradictory. In this paper, I aim to defend standpoint epistemologies from this challenge. This defense is based on a distinction between subjective and objective justifications. According to the former, a subject S is subjectively justified in believing a proposition P iff S's belief in (...) P coheres with S's pre-existing beliefs. According to the latter, a subject S is objectively justified in believing a proposition P iff S's subjective justification is truth-conducive. I argue that, if the situated knowledge thesis is spelled out in terms of subjective justification, and if the thesis of epistemic privilege is spelled out in terms of objective justification, then there is no contradiction. If this line of thought is correct, then the bias paradox does not show that standpoint epistemologies are self-contradictory. (shrink)