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  1. Computer Says I Don’t Know: An Empirical Approach to Capture Moral Uncertainty in Artificial Intelligence.Andreia Martinho, Maarten Kroesen & Caspar Chorus - 2021 - Minds and Machines 31 (2):215-237.
    As AI Systems become increasingly autonomous, they are expected to engage in decision-making processes that have moral implications. In this research we integrate theoretical and empirical lines of thought to address the matters of moral reasoning and moral uncertainty in AI Systems. We reconceptualize the metanormative framework for decision-making under moral uncertainty and we operationalize it through a latent class choice model. The core idea being that moral heterogeneity in society can be codified in terms of a small number of (...)
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  • Value Sensitive Design to Achieve the UN SDGs with AI: A Case of Elderly Care Robots.Steven Umbrello, Marianna Capasso, Maurizio Balistreri, Alberto Pirni & Federica Merenda - 2021 - Minds and Machines 31 (3):395-419.
    Healthcare is becoming increasingly automated with the development and deployment of care robots. There are many benefits to care robots but they also pose many challenging ethical issues. This paper takes care robots for the elderly as the subject of analysis, building on previous literature in the domain of the ethics and design of care robots. Using the value sensitive design approach to technology design, this paper extends its application to care robots by integrating the values of care, values that (...)
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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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  • 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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  • Who is Afraid of Black Box Algorithms? On the Epistemological and Ethical Basis of Trust in Medical AI.Juan Manuel Durán & Karin Rolanda Jongsma - 2021 - Journal of Medical Ethics 47 (5):medethics-2020-106820.
    The use of black box algorithms in medicine has raised scholarly concerns due to their opaqueness and lack of trustworthiness. Concerns about potential bias, accountability and responsibility, patient autonomy and compromised trust transpire with black box algorithms. These worries connect epistemic concerns with normative issues. In this paper, we outline that black box algorithms are less problematic for epistemic reasons than many scholars seem to believe. By outlining that more transparency in algorithms is not always necessary, and by explaining that (...)
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  • Reckoning with Assessment: Can We Responsibly Innovate? [REVIEW]Steven Umbrello - 2021 - Metascience 30 (1):41-43.
    A new edited volume by Emad Yaghmaei and Ibo van de Poel, Assessment of Responsible Innovation: Methods and Practices, is reviewed. Responsible innovation (RI) is a project into the ethical and design issues that emerge during the engineering programs of new technologies. This volume is intended to determine how if at all, RI practices can be validated and assessed for success in context.
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  • Dissecting Scientific Explanation in AI (sXAI): A Case for Medicine and Healthcare.Juan M. Durán - 2021 - Artificial Intelligence 297:103498.
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