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  1. Anticipatory ethics for emerging technologies.Philip A. E. Brey - 2012 - NanoEthics 6 (1):1-13.
    Abstract In this essay, a new approach for the ethical study of emerging technology ethics will be presented, called anticipatory technology ethics (ATE). The ethics of emerging technology is the study of ethical issues at the R&D and introduction stage of technology development through anticipation of possible future devices, applications, and social consequences. I will argue that a major problem for its development is the problem of uncertainty, which can only be overcome through methodologically sound forecasting and futures studies. I (...)
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  • AI Ethics Is Not a Panacea.Stuart McLennan, Meredith M. Lee, Amelia Fiske & Leo Anthony Celi - 2020 - American Journal of Bioethics 20 (11):20-22.
    From machine learning and computer vision to robotics and natural language processing, the application of data science and artificial intelligence is expected to transform health care (Ce...
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  • An Ethical Framework to Nowhere.Eric S. Swirsky, Carol Gu & Andrew D. Boyd - 2020 - American Journal of Bioethics 20 (11):30-32.
    In their article, Char et al. have created a model intended to tidy up the messy landscape of ethical concerns arising from machine-learning health care applications. The novel con...
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  • It is Time for Bioethicists to Enter the Arena of Machine Learning Ethics.Michaela Hardt & Marshall H. Chin - 2020 - American Journal of Bioethics 20 (11):18-20.
    Increasingly, data scientists are training machine-learning models for diagnosis, treatment selection, and resource allocation. The U.S. Food and Drug Administration has given regulatory appro...
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  • What Counts as “Clinical Data” in Machine Learning Healthcare Applications?Joshua August Skorburg - 2020 - American Journal of Bioethics 20 (11):27-30.
    Peer commentary on Char, Abràmoff & Feudtner (2020) target article: "Identifying Ethical Considerations for Machine Learning Healthcare Applications" .
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  • Deepening the Normative Evaluation of Machine Learning Healthcare Application by Complementing Ethical Considerations with Regulatory Governance.Calvin Wai-Loon Ho - 2020 - American Journal of Bioethics 20 (11):43-45.
    The pipeline model framework proposed by Char et al. makes a timely contribution to the literature in allowing one to take a step back and consider machine learning healthcare app...
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  • What Values in Design? The Challenge of Incorporating Moral Values into Design.Noëmi Manders-Huits - 2011 - Science and Engineering Ethics 17 (2):271-287.
    Recently, there is increased attention to the integration of moral values into the conception, design, and development of emerging IT. The most reviewed approach for this purpose in ethics and technology so far is Value-Sensitive Design (VSD). This article considers VSD as the prime candidate for implementing normative considerations into design. Its methodology is considered from a conceptual, analytical, normative perspective. The focus here is on the suitability of VSD for integrating moral values into the design of technologies in a (...)
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  • Machine Learning in Healthcare: Exceptional Technologies Require Exceptional Ethics.Kristine Bærøe, Maarten Jansen & Angeliki Kerasidou - 2020 - American Journal of Bioethics 20 (11):48-51.
    Char et al. describe an interesting and useful approach in their paper, “Identifying ethical considerations for machine learning healthcare applications.” Their proposed framework, which see...
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  • Where Bioethics Meets Machine Ethics.Anna C. F. Lewis - 2020 - American Journal of Bioethics 20 (11):22-24.
    Char et al. question the extent and degree to which machine learning applications should be treated as exceptional by ethicists. It is clear that of the suite of ethical issues raised by mac...
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  • What’s in the Box?: Uncertain Accountability of Machine Learning Applications in Healthcare.Ma'N. Zawati & Michael Lang - 2020 - American Journal of Bioethics 20 (11):37-40.
    Machine learning is an increasingly significant part of modern healthcare, transforming the way clinical decisions are made and health resources are managed. These developme...
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  • An Evaluation of the Pipeline Framework for Ethical Considerations in Machine Learning Healthcare Applications: The Case of Prediction from Functional Neuroimaging Data.Dawson J. Overton - 2020 - American Journal of Bioethics 20 (11):56-58.
    The pipeline framework for identifying ethical issues in machine learning healthcare applications outlined by Char et al. is a very useful starting point for the systematic consideration...
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  • Machine Learning Healthcare Applications (ML-HCAs) Are No Stand-Alone Systems but Part of an Ecosystem – A Broader Ethical and Health Technology Assessment Approach is Needed.Helene Gerhards, Karsten Weber, Uta Bittner & Heiner Fangerau - 2020 - American Journal of Bioethics 20 (11):46-48.
    ML-HCAs have the potential to significantly change an entire healthcare system. It is not even necessary to presume that this will be disruptive but sufficient to assume that the mere adaptation of...
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  • Keeping the Patient at the Center of Machine Learning in Healthcare.Jess Findley, Andrew Woods, Christopher Robertson & Marv Slepian - 2020 - American Journal of Bioethics 20 (11):54-56.
    Char et al. aspire to provide “a systematic approach to identifying … ethical concerns” around machine learning healthcare applications, which includes artificial intelligence and...
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  • Accountability in the Machine Learning Pipeline: The Critical Role of Research Ethics Oversight.Melissa D. McCradden, James A. Anderson & Randi Zlotnik Shaul - 2020 - American Journal of Bioethics 20 (11):40-42.
    Char and colleagues provide a useful conceptual framework for the proactive identification of ethical issues arising throughout the lifecycle of machine learning applications in healthcare. Th...
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  • Embedded Ethics Could Help Implement the Pipeline Model Framework for Machine Learning Healthcare Applications.Amelia Fiske, Daniel Tigard, Ruth Müller, Sami Haddadin, Alena Buyx & Stuart McLennan - 2020 - American Journal of Bioethics 20 (11):32-35.
    The field of artificial intelligence (AI) ethics has exploded in recent years, with countless academics, organizations, and influencers rushing to consider how AI technology can be developed and im...
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  • Respect and Trustworthiness in the Patient-Provider-Machine Relationship: Applying a Relational Lens to Machine Learning Healthcare Applications.Stephanie A. Kraft - 2020 - American Journal of Bioethics 20 (11):51-53.
    Healthcare delivery is an interpersonal endeavor. In every clinical interaction, providers have an ethical obligation to show respect to their patients, and ideally over time these interactions lea...
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  • Addressing the “Wicked” Problems in Machine Learning Applications – Time for Bioethical Agility.Junaid Nabi - 2020 - American Journal of Bioethics 20 (11):25-27.
    “I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that.” Elon Musk AeroAstro Centennial Symposium Massachu...
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  • Structural Disparities in Data Science: A Prolegomenon for the Future of Machine Learning.Niranjan S. Karnik, Majid Afshar, Matthew M. Churpek & Marcella Nunez-Smith - 2020 - American Journal of Bioethics 20 (11):35-37.
    As disparities and data science researchers, we write in response to Char and colleagues paper on “Identifying Ethical Considerations for Machine Learning Healthcare Applications.” While the...
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