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  1. When Doctors and AI Interact: on Human Responsibility for Artificial Risks.Mario Verdicchio & Andrea Perin - 2022 - Philosophy and Technology 35 (1):1-28.
    A discussion concerning whether to conceive Artificial Intelligence systems as responsible moral entities, also known as “artificial moral agents”, has been going on for some time. In this regard, we argue that the notion of “moral agency” is to be attributed only to humans based on their autonomy and sentience, which AI systems lack. We analyze human responsibility in the presence of AI systems in terms of meaningful control and due diligence and argue against fully automated systems in medicine. With (...)
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  • Explanatory Pragmatism: A Context-Sensitive Framework for Explainable Medical AI.Diana Robinson & Rune Nyrup - 2022 - Ethics and Information Technology 24 (1).
    Explainable artificial intelligence is an emerging, multidisciplinary field of research that seeks to develop methods and tools for making AI systems more explainable or interpretable. XAI researchers increasingly recognise explainability as a context-, audience- and purpose-sensitive phenomenon, rather than a single well-defined property that can be directly measured and optimised. However, since there is currently no overarching definition of explainability, this poses a risk of miscommunication between the many different researchers within this multidisciplinary space. This is the problem we seek (...)
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  • Relative Explainability and Double Standards in Medical Decision-Making: Should Medical AI Be Subjected to Higher Standards in Medical Decision-Making Than Doctors?Saskia K. Nagel, Jan-Christoph Heilinger & Hendrik Kempt - 2022 - Ethics and Information Technology 24 (2).
    The increased presence of medical AI in clinical use raises the ethical question which standard of explainability is required for an acceptable and responsible implementation of AI-based applications in medical contexts. In this paper, we elaborate on the emerging debate surrounding the standards of explainability for medical AI. For this, we first distinguish several goods explainability is usually considered to contribute to the use of AI in general, and medical AI in specific. Second, we propose to understand the value of (...)
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  • Why Algorithmic Speed Can Be More Important Than Algorithmic Accuracy.Jakob Mainz, Lauritz Munch, Jens Christian Bjerring & Godtfredsen Sissel - forthcoming - Clinical Ethics.
    Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximize decision speed – even if the AI is less accurate than human doctors.
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  • Responsibility, Second Opinions and Peer-Disagreement: Ethical and Epistemological Challenges of Using AI in Clinical Diagnostic Contexts.Hendrik Kempt & Saskia K. Nagel - 2022 - Journal of Medical Ethics 48 (4):222-229.
    In this paper, we first classify different types of second opinions and evaluate the ethical and epistemological implications of providing those in a clinical context. Second, we discuss the issue of how artificial intelligent could replace the human cognitive labour of providing such second opinion and find that several AI reach the levels of accuracy and efficiency needed to clarify their use an urgent ethical issue. Third, we outline the normative conditions of how AI may be used as second opinion (...)
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  • Enabling Fairness in Healthcare Through Machine Learning.Geoff Keeling & Thomas Grote - 2022 - Ethics and Information Technology 24 (3).
    The use of machine learning systems for decision-support in healthcare may exacerbate health inequalities. However, recent work suggests that algorithms trained on sufficiently diverse datasets could in principle combat health inequalities. One concern about these algorithms is that their performance for patients in traditionally disadvantaged groups exceeds their performance for patients in traditionally advantaged groups. This renders the algorithmic decisions unfair relative to the standard fairness metrics in machine learning. In this paper, we defend the permissible use of affirmative algorithms; (...)
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  • On the Ethical and Epistemological Utility of Explicable AI in Medicine.Christian Herzog - 2022 - Philosophy and Technology 35 (2):1-31.
    In this article, I will argue in favor of both the ethical and epistemological utility of explanations in artificial intelligence -based medical technology. I will build on the notion of “explicability” due to Floridi, which considers both the intelligibility and accountability of AI systems to be important for truly delivering AI-powered services that strengthen autonomy, beneficence, and fairness. I maintain that explicable algorithms do, in fact, strengthen these ethical principles in medicine, e.g., in terms of direct patient–physician contact, as well (...)
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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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  • Randomised Controlled Trials in Medical AI: Ethical Considerations.Thomas Grote - forthcoming - Journal of Medical Ethics.
    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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  • On Algorithmic Fairness in Medical Practice.Thomas Grote & Geoff Keeling - 2022 - Cambridge Quarterly of Healthcare Ethics 31 (1):83-94.
    The application of machine-learning technologies to medical practice promises to enhance the capabilities of healthcare professionals in the assessment, diagnosis, and treatment, of medical conditions. However, there is growing concern that algorithmic bias may perpetuate or exacerbate existing health inequalities. Hence, it matters that we make precise the different respects in which algorithmic bias can arise in medicine, and also make clear the normative relevance of these different kinds of algorithmic bias for broader questions about justice and fairness in healthcare. (...)
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  • 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 Deception of Certainty: How Non-Interpretable Machine Learning Outcomes Challenge the Epistemic Authority of Physicians. A Deliberative-Relational Approach.Florian Funer - 2022 - Medicine, Health Care and Philosophy 25 (2):167-178.
    Developments in Machine Learning have attracted attention in a wide range of healthcare fields to improve medical practice and the benefit of patients. Particularly, this should be achieved by providing more or less automated decision recommendations to the treating physician. However, some hopes placed in ML for healthcare seem to be disappointed, at least in part, by a lack of transparency or traceability. Skepticism exists primarily in the fact that the physician, as the person responsible for diagnosis, therapy, and care, (...)
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  • Accuracy and Interpretability: Struggling with the Epistemic Foundations of Machine Learning-Generated Medical Information and Their Practical Implications for the Doctor-Patient Relationship.Florian Funer - 2022 - Philosophy and Technology 35 (1):1-20.
    The initial successes in recent years in harnessing machine learning technologies to improve medical practice and benefit patients have attracted attention in a wide range of healthcare fields. Particularly, it should be achieved by providing automated decision recommendations to the treating clinician. Some hopes placed in such ML-based systems for healthcare, however, seem to be unwarranted, at least partially because of their inherent lack of transparency, although their results seem convincing in accuracy and reliability. Skepticism arises when the physician as (...)
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  • Design Publicity of Black Box Algorithms: A Support to the Epistemic and Ethical Justifications of Medical AI Systems.Andrea Ferrario - 2022 - Journal of Medical Ethics 48 (7):492-494.
    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 - 2022 - Journal of Medical Ethics 48 (3):175-183.
    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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  • 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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  • How Does Responsible Research & Innovation Apply to the Concept of the Digital Self, in Consideration of Privacy, Ownership and Democracy?Sijmen van Schagen - unknown
    This master thesis studies to what degree Responsible Research & Innovation can be applied to the concept of the Digital Self. In order to examine this properly, it focuses on aspects of privacy, ownership and democracy. This work is inspired by the digital health domain, where a growing number of patients become enabled to benefit from AI-powered clinical decision sup port. Aim of this study is to provide insight into what cases can be considered for exploring new design requirements for (...)
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