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2021 Utah L. Rev. 925 (2021)
Problematic Interactions between AI and Health Privacy

handle is hein.journals/utahlr2021 and id is 922 raw text is: PROBLEMATIC INTERACTIONS BETWEEN Al AND HEALTH PRIVACY W. Nicholson Price II* The interaction of artificial intelligence (Al) and health privacy is a two-way street. Both directions are problematic. This Article makes two main points. First, the advent of artificial intelligence weakens the legal protections for health privacy by rendering deidentification less reliable and by inferring health information from unprotected data sources. Second, the legal rules that protect health privacy nonetheless detrimentally impact the development of Al used in the health system by introducing multiple sources of bias: collection and sharing of data by a small set of entities, the process of data collection while following privacy rules, and the use of non-health data to infer health information. The result is an unfortunate anti- synergy: privacy protections are weak and illusory, but rules meant to protect privacy hinder other socially valuable goals. This state of affairs creates biases in health AI, privileges commercial research over academic research, and is ill-suited to either improve health care or protect patients' privacy. The ongoing dysfunction calls for a new bargain between patients and the health system about the uses of patient data. I. IMPACT OF Al ON MEDICAL PRIVACY Consider first the impact of artificial intelligence on medical privacy. The advent of artificial intelligence-alongside the big data with which it is trained and on which it operates-weakens mechanisms used to protect medical data privacy in at least two ways. First, Al enables actors with big data and sufficient computing capacity to work around deidentification, a key front-line protection for patient health data. Second, by enabling accurate and sophisticated inferences about health information from large sets of data that are not obviously tied to health, Al reduces the efficacy of trying to protect (or even identify what counts as) health data. * © W. Nicholson Price II. Professor of Law, University of Michigan Law School; Core Partner, Centre for Advanced Studies in Biomedical Innovation Law at the University of Copenhagen; and Co-PI, Project on Precision Medicine, Al, and the Law at the Petrie-Flom Center at Harvard Law School. Many thanks to Leslie Francis, Anya Prince, Alexandra Roberts, Kayte Spector-Bagdady, and Charlotte Tschider for thoughtful comments on earlier drafts and to the participants of the 2020 Lee E. Teitelbaum Utah Law Review Symposium for helpful discussion. Thanks also to the editors of the Utah Law Review for careful editing. This work was supported by the National Cancer Institute (1 RO1 CA214829-01A1) and the Novo Nordisk Foundation (NNF17SA0027784). All errors are my own. 925

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