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  1. Committing Crimes with BCIs: How Brain-Computer Interface Users Can Satisfy Actus Reus and Be Criminally Responsible.Kramer Thompson - 2021 - Neuroethics 14:311-322.
    Brain-computer interfaces allow agents to control computers without moving their bodies. The agents imagine certain things and the brain-computer interfaces read the concomitant neural activity and operate the computer accordingly. But the use of brain-computer interfaces is problematic for criminal law, which requires that someone can only be found criminally responsible if they have satisfied the actus reus requirement: that the agent has performed some (suitably specified) conduct. Agents who affect the world using brain-computer interfaces do not obviously perform any (...)
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  • Towards a Governance Framework for Brain Data.Marcello Ienca, Joseph J. Fins, Ralf J. Jox, Fabrice Jotterand, Silja Voeneky, Roberto Andorno, Tonio Ball, Claude Castelluccia, Ricardo Chavarriaga, Hervé Chneiweiss, Agata Ferretti, Orsolya Friedrich, Samia Hurst, Grischa Merkel, Fruzsina Molnár-Gábor, Jean-Marc Rickli, James Scheibner, Effy Vayena, Rafael Yuste & Philipp Kellmeyer - 2022 - Neuroethics 15 (2):1-14.
    The increasing availability of brain data within and outside the biomedical field, combined with the application of artificial intelligence to brain data analysis, poses a challenge for ethics and governance. We identify distinctive ethical implications of brain data acquisition and processing, and outline a multi-level governance framework. This framework is aimed at maximizing the benefits of facilitated brain data collection and further processing for science and medicine whilst minimizing risks and preventing harmful use. The framework consists of four primary areas (...)
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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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