A powerful argument against the counterfactual comparative account of harm is that it cannot distinguish harming from failing to benefit. In reply to this problem, I suggest a new account of harm. The account is a counterfactual comparative one, but it counts as harms only those events that make a person occupy his level of well-being at the world at which the event occurs. This account distinguishes harming from failing to benefit in a way that accommodates our intuitions about the (...) standard problem cases. In laying the groundwork for this account, I also demonstrate that rival accounts of harm are able to distinguish harming from failing to benefit only if, and because, they also appeal to the distinction between making upshots happen and allowing upshots to happen. One important implication of my discussion is that preserving the moral asymmetry between harming and failing to benefit requires a commitment to the existence of a metaphysical and moral distinction between making and allowing. (shrink)

A common criticism of the use of algorithms in criminal justice is that algorithms and their determinations are in some sense ‘opaque’—that is, difficult or impossible to understand, whether because of their complexity or because of intellectual property protections. Scholars have noted some key problems with opacity, including that opacity can mask unfair treatment and threaten public accountability. In this paper, we explore a different but related concern with algorithmic opacity, which centers on the role of public trust in grounding (...) the legitimacy of criminal justice institutions. We argue that algorithmic opacity threatens the trustworthiness of criminal justice institutions, which in turn threatens their legitimacy. We first offer an account of institutional trustworthiness before showing how opacity threatens to undermine an institution’s trustworthiness. We then explore how threats to trustworthiness affect institutional legitimacy. Finally, we offer some policy recommendations to mitigate the threat to trustworthiness posed by the opacity problem. (shrink)

To many, the idea of autonomous weapons systems (AWS) killing human beings is grotesque. Yet critics have had difficulty explaining why it should make a significant moral difference if a human combatant is killed by an AWS as opposed to being killed by a human combatant. The purpose of this paper is to explore the roots of various deontological concerns with AWS and to consider whether these concerns are distinct from any concerns that also apply to long- distance, human-guided weaponry. (...) We suggest that at least one major driver of the intuitive moral aversion to lethal AWS is that their use disrespects their human targets by violating the martial contract between human combatants. On our understanding of this doctrine, service personnel cede a right not to be directly targeted with lethal violence to other human agents alone. Artificial agents, of which AWS are one example, cannot understand the value of human life. A human combatant cannot transfer his privileges of targeting enemy combatants to a robot. Therefore, the human duty-holder who deploys AWS breaches the martial contract between human combatants and disrespects the targeted combatants. We consider whether this novel deontological objection to AWS forms the foundation of several other popular yet imperfect deontological objections to AWS. (shrink)

Recent studies have shown that recidivism scoring algorithms like COMPAS have significant racial bias: Black defendants are roughly twice as likely as white defendants to be mistakenly classified as medium- or high-risk. This has led some to call for abolishing COMPAS. But many others have argued that algorithms should instead be given access to a defendant's race, which, perhaps counterintuitively, is likely to improve outcomes. This approach can involve either establishing race-sensitive risk thresholds, or distinct racial ‘tracks’. Is there a (...) moral difference between these two approaches? We first consider Deborah Hellman's view that the use of distinct racial tracks (but not distinct thresholds) does not constitute disparate treatment since the effects on individuals are indirect and does not rely on a racial generalization. We argue that this is mistaken: the use of different racial tracks seems both to have direct effects on and to rely on a racial generalization. We then offer an alternative understanding of the distinction between these two approaches—namely, that the use of different cut points is to the counterfactual comparative disadvantage, ex ante, of all white defendants, while the use of different racial tracks can in principle be to the advantage of all groups, though some defendants in both groups will fare worse. Does this mean that the use of cut points is impermissible? Ultimately, we argue, while there are reasons to be skeptical of the use of distinct cut points, it is an open question whether these reasons suffice to make a difference to their moral permissibility. (shrink)

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” emcompasses 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. (shrink)