Friedman, B. and Nissenbaum, H., “Bias in Computer Systems,” 1996, available at <https://nissenbaum.tech.cornell.edu/papers/Bias%20in%20Computer%20Systems.pdf> (last visited December 6, 2021).+(last+visited+December+6,+2021).>Google Scholar

Caliskan, A., Bryson, J.J., and Narayanan, A., “Semantics Derived Automatically from Language Corpora Contain Human-Like Biases,” Science 356, no. 6334 (2017): 183–186; A. Hadhazy, “Biased Bots: Artificial-Intelligence Systems Echo Human Prejudices,” Princeton University (April 2017): at 18; T. McSweeny, “Psychographics, Predictive Analytics, Artificial Intelligence, & Bots: Is The FTC Keeping Pace?” Georgetown Law Technology Review 2 (2018): 514, 516, 530; J. Buolamwini and T. Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” paper presented at Conference on Fairness, Accountability and Transparency, 2018; I.A. Hamilton, “Why It’s Totally Unsurprising That Amazon’s Recruitment AI Was Biased against Women,” Business Insider, 2018, available at <https://www.businessinsider.com/amazon-ai-biased-against-women-no-surprise-sandra-wachter-2018-10> (last visited December 6, 2021); T. Brennan, W. Dieterich, B. Ehret, “Evaluating the Predictive Validity of the COMPAS Risk and Needs Assessment System,” Criminal Justice and Behavior 36, no. 1 (2009): 21-40; J. Dressel and H. Farid. “The Accuracy, Fairness, and Limits of Predicting Recidivism,” Science Advances 4, no. 1 (2018): eaao5580.CrossRefGoogle ScholarPubMed

Ryan-Mosely, T., “The New Lawsuit That Shows Facial Recogntion Is Officially a Civil Rights Issue,” MIT Technology Review (2021).Google Scholar

Ross, C., “As the FDA Clears a Flood of AI Tools, Missing Data Raise Troubling Questions on Safety and Fairness, “ STAT, February 3, 2021, available at <https://www.statnews.com/2021/02/03/fda-clearances-artificial-intelligence-data/> (last visited December 6, 2021).Google Scholar

Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S., “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations,” Science 366, no. 6464 (2019): 447–453.CrossRefGoogle Scholar

European Commission, “Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) and Amending Certain Union Legislative Acts,” 2021, available at <https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52021PC0206> (last visited December 6, 2021).+(last+visited+December+6,+2021).>Google Scholar

Ansell, D.A. and McDonald, E.K., “Bias, Black Lives, and Academic Medicine,” New England Journal of Medicine 372, no. 12 (2015): 1087–1089; Price, I., Nicholson, W., “Medical AI and Contextual Bias,” 2019.CrossRefGoogle ScholarPubMed

van Meeteren, J., Maltais, S., Dunlay, S.M., et al., “A Multi-Institutional Outcome Analysis of Patients Undergoing Left Ventricular Assist Device Implantation Stratified by Sex and Race,” Journal of Heart and Lung Transplantation 36, no. 1 (2017): 64–70.CrossRefGoogle Scholar

Molina, E.J., Shah, P., Kiernan, M.S., et al., “The Society of Thoracic Surgeons Intermacs 2020 Annual Report,” Annals of Thoracic Surgery 111, no. 3 (2021): 778–792.CrossRefGoogle Scholar

Goyal, P., Paul, T., Almarzooq, Z.I., et al., “Sex‐and Race‐Related Differences in Characteristics and Outcomes of Hospitalizations for Heart Failure with Preserved Ejection Fraction,” Journal of the American Heart Association 6, no. 4 (2017): e003330.CrossRefGoogle ScholarPubMed

Id. Google Scholar

Ueyama, H., Malik, A., Kuno, T., et al., “Racial Disparities in Hospital Outcomes after Left Ventricular Assist Device Implantation,” Journal of Cardiac Surgery 35, no. 10 (2020): 2633–2639.CrossRefGoogle ScholarPubMed

Lui, C., Fraser, C.D. III, Zhou, X., et al., “Racial Disparities in Patients Bridged to Heart Transplantation with Left Ventricular Assist Devices,” Annals of Thoracic Surgery 108, no. 4 (2019): 1122–1126.CrossRefGoogle ScholarPubMed

Ueyma et al., supra note 12.Google Scholar

Okoh, A.K., Selevanny, M., Singh, S., et al., “Racial Disparities and Outcomes of Left Ventricular Assist Device Implantation as a Bridge to Heart Transplantation,” ESC Heart Failure 7, no. 5 (2020): 2744–2751.CrossRefGoogle ScholarPubMed

Jaiswal, A., Truby, L.K., Chichra, A., et al., “Impact of Obesity on Ventricular Assist Device Outcomes,” Journal of Cardiac Failure 26, no. 4 (2020): 287–297.CrossRefGoogle ScholarPubMed

Okoh et al., supra note 15.Google Scholar

Khan, M.S., Yuzefpolskaya, M., Memon, M.M., et al., “Outcomes Associated with Obesity in Patients Undergoing Left Ventricular Assist Device Implantation: A Systematic Review and Meta-analysis,” Asaio Journal 66, no. 4 (2020): 401–408.CrossRefGoogle ScholarPubMed

Ueyma et al., supra note 12.Google Scholar

van Meeteren et al., supra note 8; Itoh, A., “Impact of Age, Sex, Therapeutic Intent, Race and Severity of Advanced Heart Failure on Short-Term Principal Outcomes in the MOMENTUM 3 Trial,” Journal of Heart and Lung Transplantation 37, no. 1 (2018): 7–14.Google Scholar

Id.; Ueyma et al., supra note 12.Google Scholar

Bowles, E.J.A., Wellman, R., Feigelson, H.S., et al., “Risk of Heart Failure in Breast Cancer Patients after Anthracycline and Trastuzumab Treatment: A Retrospective Cohort Study,” Journal of the National Cancer Institute 104, no. 17 (2012): 1293–1305.CrossRefGoogle ScholarPubMed

Okoh et al., supra note 15.Google Scholar

Sheikh, F.H., Ravichandran, A.K., Goldstein, D.J., et al., “Impact of Race on Clinical Outcomes after Implantation with a Fully Magnetically Levitated Left Ventricular Assist Device: An Analysis From the MOMENTUM 3 Trial,” Circulation: Heart Failure 14, no. 10 (2021): e008360.Google ScholarPubMed

Ueyma et al., supra note 12.Google Scholar

Tsiouris, A., Brewer, R.J., Borgi, J., Nemeh, H., Paone, G., and Morgan, J.A., “Continuous-Flow Left Ventricular Assist Device Implantation as a Bridge to Transplantation or Destination Therapy: Racial Disparities in Outcomes,” Journal of Heart and Lung Transplantation 32, no. 3 (2013): 299–304; X. Wang, A.A. Luke, J.M. Vader, T.M. Maddox, K.E. Joynt Maddox , “Disparities and Impact of Medicaid Expansion on Left Ventricular Assist Device Implantation and Outcomes,” Circulation: Cardiovascular Quality and Outcomes 13, no. 6 (2020): e006284.CrossRefGoogle ScholarPubMed

Okoh et al., supra note 15; Tsiouris et al., supra note 26.Google Scholar

Ueyma et al., supra note 12.Google Scholar

Id.; Breathett, K., Yee, E., Pool, N., et al., “Does Race Influence Decision Making for Advanced Heart Failure Therapies?” Journal of the American Heart Association 8, no. 22 (2019): e013592.CrossRefGoogle ScholarPubMed

Bui, Q.M., Allen, L.A., LeMond, L., Brambatti, M., Adler, E., “Psychosocial Evaluation of Candidates for Heart Transplant and Ventricular Assist Devices: Beyond the Current Consensus,” Circulation: Heart Failure 12, no. 7 (2019): e006058.Google ScholarPubMed

Steinberg, R.S., Nayak, A., Dong, T., Morris, A.A., “Primary Caregiver Relationships for Advanced Heart Failure Therapy Differ Based on Sex and Race and Predict Eligibility,” Journal of Cardiac Failure 26, no. 10 (2020): S10.CrossRefGoogle Scholar

Takshi, S., “Unexpected Inequality: Disparate-Impact from Artificial Intelligence in Healthcare Decisions,” Journal of Law and Health 34, no. 2 (2021): 215.Google ScholarPubMed

Selbst, A.D., “Negligence and AI/MLs Human Users,” Boston University Law Review 100, no. 1315 (2020): 1354–1360.Google Scholar

Price, W.N. II, Sachs, R., and Eisenberg, R.S., “New Innovation Models in Medical AI,” Washington University Law Review (forthcoming 2022); S. Gerke, B. Babic, T. Evgeniou, and I.G. Cohen, “The Need for a System View to Regulate Artificial Intelligence/Machine Learning-Based Software as Medical Device,” npj: Digital Medicine 3, no. 1 (2020): 53.Google Scholar

Ross, supra note 4.Google Scholar

Federal Trade Commission. Aiming for truth, fairness, and equity in your company’s use of AI. 2021.Google Scholar

Mathews, M.E.A.W., “New York Regulator Probes UnitedHealth Algorithm for Racial Bias,” Wall Street Journal, Octover 26, 2019.Google Scholar

White House, Executive Order on Maintaining American Leadership in Artificial Intelligence, 2019.Google Scholar

Cohen, I.G., Evgeniou, T., Gerke, S., Minssen, T., “The European Artificial Intelligence Strategy: Implications and Challenges for Digital Health,” The Lancet Digital Health 2, no. 7 (2020): e376-e379; AI HLEG, Policy and Investment Recommendations for Trustworthy AI, 2019; European Commission, White Paper on Artificial Intelligence—A European Approach to Excellence and Trust, 2020.CrossRefGoogle ScholarPubMed

SPDP Commission, Infocomm Media Development Agency Proposed Model AI Governance Framework, 2020.Google Scholar

Microsoft, Microsoft AI principles, 2021.Google Scholar

Partnership on AI, Tenets, June 15, 2021.Google Scholar

European Commision, supra note 6.Google Scholar

See supra note 7; also Gerke et al., supra note 34.Google Scholar

Gerke et al., supra note 34.Google Scholar

Nayak, A., Hicks, A.J., and Morris, A.A. , “Understanding the Complexity of Heart Failure Risk and Treatment in Black Patients,” Circulation: Heart Failure 13, no. 8 (2020): e007264.Google ScholarPubMed

Sepucha, K.R., Abhyankar, P., Hoffman, A.S., et al., “Standards for Universal Reporting of Patient Decision Aid Evaluation Studies: The Development of SUNDAE Checklist,” BMJ Quality & Safety 27, no. 5 (2018): 380–388.CrossRefGoogle ScholarPubMed

Lee, N.T., Resnick, P., and Barton, G., “Algorithmic Bias Detection and Mitigation: Best Practices and Policies to Reduce Consumer Harms,” Brookings Institute, 2019.Google Scholar

Price et al., supra note 7.Google Scholar

Friedman and Nissenbaum, supra note 1.Google Scholar

Lui, supra note 7.Google Scholar

Gebru, T., Morgenstern, J., Vecchione, B., et al. “Datasheets for Datasets,” 2018, Cornell University, available at <https://arxiv.org/abs/1803.09010> (last visited December 6, 2021).+(last+visited+December+6,+2021).>Google Scholar

Moss, E., Watkins, E.A., Singh, S., Elish, M.C., and Metcalf, J., “Assembling Accountability: Algorithmic Impact Assessment for the Public Interest,” 2021, available at <https://datasociety.net/library/assembling-accountability-algorithmic-impact-assessment-for-the-public-interest/> (last visited December 6, 2021).+(last+visited+December+6,+2021).>Google Scholar

Flanagin, A., Frey, T., Christiansen, S.L., “Committee AMoS. Updated Guidance on the Reporting of Race and Ethnicity in Medical and Science Journals,” JAMA 326, no. 7 (2021): 621–627.CrossRefGoogle Scholar

Cunningham, P. and Delany, S.J., “Algorithmic Bias and Regularisation in Machine Learning,” 2020, Cornell University, available at <https://arxiv.org/abs/2005.09052arXiv preprint arXiv:2005.09052> (last visited December 6, 2021).+(last+visited+December+6,+2021).>Google Scholar