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Duncan Purves
University of Florida
  1. Harming as Making Worse Off.Duncan Purves - 2019 - Philosophical Studies 176 (10):2629-2656.
    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 (...)
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  2. Autonomous Weapons Systems and the Moral Equality of Combatants.Michael Skerker, Duncan Purves & Ryan Jenkins - 2020 - Ethics and Information Technology 3 (6).
    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. (...)
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    Five Ethical Challenges for Data-Driven Policing.Jeremy Davis, Duncan Purves, Juan Gilbert & Schuyler Sturm - forthcoming - AI and Ethics.
    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, (...)
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  4.  51
    Public Trust, Institutional Legitimacy, and the Use of Algorithms in Criminal Justice.Duncan Purves & Jeremy Davis - forthcoming - Public Affairs Quarterly.
    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 (...)
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