- Explanatory pragmatism: a context-sensitive framework for explainable medical AI.Diana Robinson & Rune Nyrup - 2022 - Ethics and Information Technology 24 (1).details
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How the machine ‘thinks’: Understanding opacity in machine learning algorithms.Jenna Burrell - 2016 - Big Data and Society 3 (1):205395171562251.details
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Algorithmic bias: on the implicit biases of social technology.Gabbrielle M. Johnson - 2020 - Synthese 198 (10):9941-9961.details
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Transparency in Complex Computational Systems.Kathleen A. Creel - 2020 - Philosophy of Science 87 (4):568-589.details
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Mapping Value Sensitive Design onto AI for Social Good Principles.Steven Umbrello & Ibo van de Poel - 2021 - AI and Ethics 1 (3):283–296.details
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Transparency in Algorithmic and Human Decision-Making: Is There a Double Standard?John Zerilli, Alistair Knott, James Maclaurin & Colin Gavaghan - 2018 - Philosophy and Technology 32 (4):661-683.details
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On the ethics of algorithmic decision-making in healthcare.Thomas Grote & Philipp Berens - 2020 - Journal of Medical Ethics 46 (3):205-211.details
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Principles of Biomedical Ethics: Marking Its Fortieth Anniversary.James Childress & Tom Beauchamp - 2019 - American Journal of Bioethics 19 (11):9-12.details
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Understanding from Machine Learning Models.Emily Sullivan - 2022 - British Journal for the Philosophy of Science 73 (1):109-133.details
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(2 other versions)Personal Knowledge.Michael Polanyi - 1958 - Chicago,: Routledge.details
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Inductive Risk, Epistemic Risk, and Overdiagnosis of Disease.Justin B. Biddle - 2016 - Perspectives on Science 24 (2):192-205.details
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(2 other versions)Personal knowledge.Michael Polanyi - 1958 - Chicago,: University of Chicago Press.details
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Dermatologist-level classification of skin cancer with deep neural networks.Andre Esteva, Brett Kuprel, Roberto A. Novoa, Justin Ko, Susan M. Swetter, Helen M. Blau & Sebastian Thrun - 2017 - Nature 542 (7639):115-118.details
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead.Cynthia Rudin - 2019 - Nature Machine Intelligence 1.details
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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.details
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Theory choice, non-epistemic values, and machine learning.Ravit Dotan - 2020 - Synthese (11):1-21.details
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Grounds for Trust: Essential Epistemic Opacity and Computational Reliabilism.Juan M. Durán & Nico Formanek - 2018 - Minds and Machines 28 (4):645-666.details
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Solving the Black Box Problem: A Normative Framework for Explainable Artificial Intelligence.Carlos Zednik - 2019 - Philosophy and Technology 34 (2):265-288.details
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Epistemic risks in cancer screening: Implications for ethics and policy.Justin B. Biddle - 2020 - Studies in History and Philosophy of Science Part C: Studies in History and Philosophy of Biological and Biomedical Sciences 79:101200.details
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Dissecting scientific explanation in AI (sXAI): A case for medicine and healthcare.Juan M. Durán - 2021 - Artificial Intelligence 297 (C):103498.details
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Responsibility beyond design: Physicians’ requirements for ethical medical AI.Martin Sand, Juan Manuel Durán & Karin Rolanda Jongsma - 2021 - Bioethics 36 (2):162-169.details
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Artificial Intelligence and Black‐Box Medical Decisions: Accuracy versus Explainability.Alex John London - 2019 - Hastings Center Report 49 (1):15-21.details
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Randomized Controlled Trials in Medical AI.Konstantin Genin & Thomas Grote - 2021 - Philosophy of Medicine 2 (1).details
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