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  1.  61
    Mitigating Racial Bias in Machine Learning.Kristin M. Kostick-Quenet, I. Glenn Cohen, Sara Gerke, Bernard Lo, James Antaki, Faezah Movahedi, Hasna Njah, Lauren Schoen, Jerry E. Estep & J. S. Blumenthal-Barby - 2022 - Journal of Law, Medicine and Ethics 50 (1):92-100.
    When applied in the health sector, AI-based applications raise not only ethical but legal and safety concerns, where algorithms trained on data from majority populations can generate less accurate or reliable results for minorities and other disadvantaged groups.
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  2.  34
    Rise of the Bioethics AI: Curse or Blessing?Craig M. Klugman & Sara Gerke - 2022 - American Journal of Bioethics 22 (7):35-37.
    In October 2021, the Allen Institute for Artificial Intelligence publicly released Delphi, an artificial intelligence system trained to make general moral decisions (Allen Institute for Artifi...
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  3.  83
    Synthetic Health Data: Real Ethical Promise and Peril.Daniel Susser, Daniel S. Schiff, Sara Gerke, Laura Y. Cabrera, I. Glenn Cohen, Megan Doerr, Jordan Harrod, Kristin Kostick-Quenet, Jasmine McNealy, Michelle N. Meyer, W. Nicholson Price & Jennifer K. Wagner - 2024 - Hastings Center Report 54 (5):8-13.
    Researchers and practitioners are increasingly using machine‐generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood (...)
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  4. Algorithms on Regulatory Lockdown in Medicine.Boris Babic, Sara Gerke, Theodoros Evgeniou & I. Glenn Cohen - 2019 - Science 6470 (366):1202-1204.
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  5. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device.Sara Gerke, Boris Babic, Theodoros Evgeniou & I. Glenn Cohen - 2020 - Nature Digital Medicine 53 (3):1-4.
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  6.  45
    AI Surveillance during Pandemics: Ethical Implementation Imperatives.Carmel Shachar, Sara Gerke & Eli Y. Adashi - 2020 - Hastings Center Report 50 (3):18-21.
    Artificial intelligence surveillance can be used to diagnose individual cases, track the spread of Covid‐19, and help provide care. The use of AI for surveillance purposes (such as detecting new Covid‐19 cases and gathering data from healthy and ill individuals) in a pandemic raises multiple concerns ranging from privacy to discrimination to access to care. Luckily, there exist several frameworks that can help guide stakeholders, especially physicians but also AI developers and public health officials, as they navigate these treacherous shoals. (...)
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  7.  24
    COVID-19 Antibody Testing as a Precondition for Employment: Ethical and Legal Considerations.Sara Gerke, Gali Katznelson, Dorit Reiss & Carmel Shachar - 2021 - Journal of Law, Medicine and Ethics 49 (2):293-302.
    Employers and governments are interested in the use of serological testing to allow people to return to work before there is a vaccine for SARS-CoV-2. We articulate the preconditions needed for the implementation of antibody testing, including the role of the U.S. Food & Drug Administration.
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  8.  6
    Synthetic Health Data: Real Ethical Promise and Peril.Daniel Susser, Daniel S. Schiff, Sara Gerke, Laura Y. Cabrera, I. Glenn Cohen, Megan Doerr, Jordan Harrod, Kristin Kostick-Quenet, Jasmine McNealy, Michelle N. Meyer, I. I. W. Nicholson Price & Jennifer K. Wagner - 2024 - Hastings Center Report 54 (5):8-13.
    Researchers and practitioners are increasingly using machine-generated synthetic data as a tool for advancing health science and practice, by expanding access to health data while—potentially—mitigating privacy and related ethical concerns around data sharing. While using synthetic data in this way holds promise, we argue that it also raises significant ethical, legal, and policy concerns, including persistent privacy and security problems, accuracy and reliability issues, worries about fairness and bias, and new regulatory challenges. The virtue of synthetic data is often understood (...)
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  9.  33
    The Development, Implementation, and Oversight of Artificial Intelligence in Health Care: Legal and Ethical Issues.Jenna Becker, Sara Gerke & I. Glenn Cohen - 2023 - In Erick Valdés & Juan Alberto Lecaros (eds.), Handbook of Bioethical Decisions. Volume I: Decisions at the Bench. Springer Verlag. pp. 441-456.
    Artificial Intelligence (AI), especially of the machine learning (ML) variety, is used by health care organizations to assist with a number of tasks, including diagnosing patients and optimizing operational workflows. AI products already proliferate the health care market, with usage increasing as the technology matures. Although AI may potentially revolutionize health care, the use of AI in health settings also leads to risks ranging from violating patient privacy to implementing a biased algorithm. This chapter begins with a broad overview of (...)
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