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  1. Biased Face Recognition Technology Used by Government: A Problem for Liberal Democracy.Michael Gentzel - 2021 - Philosophy and Technology 34 (4):1639-1663.
    This paper presents a novel philosophical analysis of the problem of law enforcement’s use of biased face recognition technology in liberal democracies. FRT programs used by law enforcement in identifying crime suspects are substantially more error-prone on facial images depicting darker skin tones and females as compared to facial images depicting Caucasian males. This bias can lead to citizens being wrongfully investigated by police along racial and gender lines. The author develops and defends “A Liberal Argument Against Biased FRT,” which (...)
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  • The Impact of Artificial Intelligence on Jobs and Work in New Zealand.James Maclaurin, Colin Gavaghan & Alistair Knott - 2021 - Wellington, New Zealand: New Zealand Law Foundation.
    Artificial Intelligence (AI) is a diverse technology. It is already having significant effects on many jobs and sectors of the economy and over the next ten to twenty years it will drive profound changes in the way New Zealanders live and work. Within the workplace AI will have three dominant effects. This report (funded by the New Zealand Law Foundation) addresses: Chapter 1 Defining the Technology of Interest; Chapter 2 The changing nature and value of work; Chapter 3 AI and (...)
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  • Limits of Trust in Medical AI.Joshua James Hatherley - 2020 - Journal of Medical Ethics 46 (7):478-481.
    Artificial intelligence is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems (...)
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  • Shared Decision‐Making and Maternity Care in the Deep Learning Age: Acknowledging and Overcoming Inherited Defeaters.Keith Begley, Cecily Begley & Valerie Smith - 2021 - Journal of Evaluation in Clinical Practice 27 (3):497–503.
    In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in (...)
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  • From Clinical Decision Support to Clinical Reasoning Support Systems.Sophie Baalen, Mieke Boon & Petra Verhoef - 2021 - Journal of Evaluation in Clinical Practice 27 (3):520-528.
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  • Randomised Controlled Trials in Medical AI: Ethical Considerations.Thomas Grote - forthcoming - Journal of Medical Ethics:medethics-2020-107166.
    In recent years, there has been a surge of high-profile publications on applications of artificial intelligence systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials—particularly randomised controlled trials —is gaining ground. However, an issue that has (...)
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  • Design Publicity of Black Box Algorithms: A Support to the Epistemic and Ethical Justifications of Medical AI Systems.Andrea Ferrario - forthcoming - Journal of Medical Ethics:medethics-2021-107482.
    In their article ‘Who is afraid of black box algorithms? On the epistemological and ethical basis of trust in medical AI’, Durán and Jongsma discuss the epistemic and ethical challenges raised by black box algorithms in medical practice. The opacity of black box algorithms is an obstacle to the trustworthiness of their outcomes. Moreover, the use of opaque algorithms is not normatively justified in medical practice. The authors introduce a formalism, called computational reliabilism, which allows generating justified beliefs on the (...)
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  • AI Support for Ethical Decision-Making Around Resuscitation: Proceed with Care.Nikola Biller-Andorno, Andrea Ferrario, Susanne Joebges, Tanja Krones, Federico Massini, Phyllis Barth, Georgios Arampatzis & Michael Krauthammer - forthcoming - Journal of Medical Ethics:medethics-2020-106786.
    Artificial intelligence systems are increasingly being used in healthcare, thanks to the high level of performance that these systems have proven to deliver. So far, clinical applications have focused on diagnosis and on prediction of outcomes. It is less clear in what way AI can or should support complex clinical decisions that crucially depend on patient preferences. In this paper, we focus on the ethical questions arising from the design, development and deployment of AI systems to support decision-making around cardiopulmonary (...)
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