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  1. Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2019 - Philosophy and Technology 32 (4):661-683.
    We are sceptical of concerns over the opacity of algorithmic decision tools. While transparency and explainability are certainly important desiderata in algorithmic governance, we worry that automated decision-making is being held to an unrealistically high standard, possibly owing to an unrealistically high estimate of the degree of transparency attainable from human decision-makers. In this paper, we review evidence demonstrating that much human decision-making is fraught with transparency problems, show in what respects AI fares little worse or better and argue that (...)
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  • Artificial Intelligence and Patient-Centered Decision-Making.Jens Christian Bjerring & Jacob Busch - 2020 - Philosophy and Technology 34 (2):349-371.
    Advanced AI systems are rapidly making their way into medical research and practice, and, arguably, it is only a matter of time before they will surpass human practitioners in terms of accuracy, reliability, and knowledge. If this is true, practitioners will have a prima facie epistemic and professional obligation to align their medical verdicts with those of advanced AI systems. However, in light of their complexity, these AI systems will often function as black boxes: the details of their contents, calculations, (...)
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  • Ethical Machine Decisions and the Input-Selection Problem.Björn Lundgren - 2021 - Synthese 199 (3-4):11423-11443.
    This article is about the role of factual uncertainty for moral decision-making as it concerns the ethics of machine decision-making. The view that is defended here is that factual uncertainties require a normative evaluation and that ethics of machine decision faces a triple-edged problem, which concerns what a machine ought to do, given its technical constraints, what decisional uncertainty is acceptable, and what trade-offs are acceptable to decrease the decisional uncertainty.
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  • Challenges in Enabling User Control Over Algorithm-Based Services.Pascal D. König - forthcoming - AI and Society.
    Algorithmic systems that provide services to people by supporting or replacing human decision-making promise greater convenience in various areas. The opacity of these applications, however, means that it is not clear how much they truly serve their users. A promising way to address the issue of possible undesired biases consists in giving users control by letting them configure a system and aligning its performance with users’ own preferences. However, as the present paper argues, this form of control over an algorithmic (...)
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  • Zombies in the Loop? Humans Trust Untrustworthy AI-Advisors for Ethical Decisions.Sebastian Krügel, Andreas Ostermaier & Matthias Uhl - 2022 - Philosophy and Technology 35 (1):1-37.
    Departing from the claim that AI needs to be trustworthy, we find that ethical advice from an AI-powered algorithm is trusted even when its users know nothing about its training data and when they learn information about it that warrants distrust. We conducted online experiments where the subjects took the role of decision-makers who received advice from an algorithm on how to deal with an ethical dilemma. We manipulated the information about the algorithm and studied its influence. Our findings suggest (...)
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  • A Sociotechnical Perspective for the Future of AI: Narratives, Inequalities, and Human Control.Andreas Theodorou & Laura Sartori - 2022 - Ethics and Information Technology 24 (1).
    Different people have different perceptions about artificial intelligence. It is extremely important to bring together all the alternative frames of thinking—from the various communities of developers, researchers, business leaders, policymakers, and citizens—to properly start acknowledging AI. This article highlights the ‘fruitful collaboration’ that sociology and AI could develop in both social and technical terms. We discuss how biases and unfairness are among the major challenges to be addressed in such a sociotechnical perspective. First, as intelligent machines reveal their nature of (...)
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  • People Prefer Moral Discretion to Algorithms: Algorithm Aversion Beyond Intransparency.Johanna Jauernig, Matthias Uhl & Gari Walkowitz - 2022 - Philosophy and Technology 35 (1):1-25.
    We explore aversion to the use of algorithms in moral decision-making. So far, this aversion has been explained mainly by the fear of opaque decisions that are potentially biased. Using incentivized experiments, we study which role the desire for human discretion in moral decision-making plays. This seems justified in light of evidence suggesting that people might not doubt the quality of algorithmic decisions, but still reject them. In our first study, we found that people prefer humans with decision-making discretion to (...)
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  • Towards a Value Sensitive Design Framework for Attaining Meaningful Human Control Over Autonomous Weapons Systems.Steven Umbrello - 2021 - Dissertation, Consortium FINO
    The international debate on the ethics and legality of autonomous weapon systems (AWS) as well as the call for a ban are primarily focused on the nebulous concept of fully autonomous AWS. More specifically, on AWS that are capable of target selection and engagement without human supervision or control. This thesis argues that such a conception of autonomy is divorced both from military planning and decision-making operations as well as the design requirements that govern AWS engineering and subsequently the tracking (...)
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  • New Horizons on Robotics: Ethics Challenges.António Moniz - 2019 - In Maria Céu do Patrão Neves (ed.), Ethics, Science and Society: Challenges for BioPolitics. Lisboa, Portugal: pp. 57-67.
    In this chapter, the focus is on robotics development and its ethical implications, especially on some particular applications or interaction principles. In recent years, such developments have happened very quickly, based on the advances achieved in the last few decades in industrial robotics. The technological developments in manufacturing, with the implementation of Industry 4.0 strategies in most industrialized countries, and the dissemination of production strategies into services and health sectors, enabled robotics to develop in a variety of new directions. Policy (...)
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  • Behavioural Artificial Intelligence: An Agenda for Systematic Empirical Studies of Artificial Inference.Tore Pedersen & Christian Johansen - 2020 - AI and Society 35 (3):519-532.
    Artificial intelligence receives attention in media as well as in academe and business. In media coverage and reporting, AI is predominantly described in contrasted terms, either as the ultimate solution to all human problems or the ultimate threat to all human existence. In academe, the focus of computer scientists is on developing systems that function, whereas philosophy scholars theorize about the implications of this functionality for human life. In the interface between technology and philosophy there is, however, one imperative aspect (...)
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  • Applying a Principle of Explicability to AI Research in Africa: Should We Do It?Mary Carman & Benjamin Rosman - 2020 - Ethics and Information Technology 23 (2):107-117.
    Developing and implementing artificial intelligence (AI) systems in an ethical manner faces several challenges specific to the kind of technology at hand, including ensuring that decision-making systems making use of machine learning are just, fair, and intelligible, and are aligned with our human values. Given that values vary across cultures, an additional ethical challenge is to ensure that these AI systems are not developed according to some unquestioned but questionable assumption of universal norms but are in fact compatible with the (...)
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  • Foundations of an Ethical Framework for AI Entities: The Ethics of Systems.Andrej Dameski - 2020 - Dissertation, University of Luxembourg
    The field of AI ethics during the current and previous decade is receiving an increasing amount of attention from all involved stakeholders: the public, science, philosophy, religious organizations, enterprises, governments, and various organizations. However, this field currently lacks consensus on scope, ethico-philosophical foundations, or common methodology. This thesis aims to contribute towards filling this gap by providing an answer to the two main research questions: first, what theory can explain moral scenarios in which AI entities are participants?; and second, what (...)
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  • Trusting Digital Technologies Correctly.Mariarosaria Taddeo - 2017 - Minds and Machines 27 (4):565-568.
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  • Engineering Equity: How AI Can Help Reduce the Harm of Implicit Bias.Ying-Tung Lin, Tzu-Wei Hung & Linus Ta-Lun Huang - 2020 - Philosophy and Technology 34 (1):65-90.
    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 (...)
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  • Coupling Levels of Abstraction in Understanding Meaningful Human Control of Autonomous Weapons: A Two-Tiered Approach.Steven Umbrello - 2021 - Ethics and Information Technology 23 (3):455-464.
    The international debate on the ethics and legality of autonomous weapon systems (AWS), along with the call for a ban, primarily focus on the nebulous concept of fully autonomous AWS. These are AWS capable of target selection and engagement absent human supervision or control. This paper argues that such a conception of autonomy is divorced from both military planning and decision-making operations; it also ignores the design requirements that govern AWS engineering and the subsequent tracking and tracing of moral responsibility. (...)
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  • On the Person-Based Predictive Policing of AI.Tzu-Wei Hung & Chun-Ping Yen - 2020 - Ethics and Information Technology 23 (3):165-176.
    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 (...)
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  • Socio-Ethical Implications of Using AI in Accelerating SDG3 in Least Developed Countries.Kutoma Wakunuma, Tilimbe Jiya & Suleiman Aliyu - 2020 - Journal of Responsible Technology 4:100006.
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  • Levels of Explainable Artificial Intelligence for Human-Aligned Conversational Explanations.Richard Dazeley, Peter Vamplew, Cameron Foale, Charlotte Young, Sunil Aryal & Francisco Cruz - 2021 - Artificial Intelligence 299:103525.
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  • Ethics of the Health-Related Internet of Things: A Narrative Review.Brent Mittelstadt - 2017 - Ethics and Information Technology 19 (3):1-19.
    The internet of things is increasingly spreading into the domain of medical and social care. Internet-enabled devices for monitoring and managing the health and well-being of users outside of traditional medical institutions have rapidly become common tools to support healthcare. Health-related internet of things (H-IoT) technologies increasingly play a key role in health management, for purposes including disease prevention, real-time tele-monitoring of patient’s functions, testing of treatments, fitness and well-being monitoring, medication dispensation, and health research data collection. H-IoT promises many (...)
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