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Governance of artificial intelligence and personal health information

Governance of artificial intelligence and personal health information This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions.Design/methodology/approachThis conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance.FindingsThis paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.Social implicationsHealth data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security.Originality/valueThis is the first paper focusing on health data governance in relation to AI/machine learning. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Digital Policy, Regulation and Governance Emerald Publishing

Governance of artificial intelligence and personal health information

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2398-5038
DOI
10.1108/dprg-08-2018-0048
Publisher site
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Abstract

This paper aims to assess the increasing challenges to governing the personal health information (PHI) essential for advancing artificial intelligence (AI) machine learning innovations in health care. Risks to privacy and justice/equity are discussed, along with potential solutions.Design/methodology/approachThis conceptual paper highlights the scale and scope of PHI data consumed by deep learning algorithms and their opacity as novel challenges to health data governance.FindingsThis paper argues that these characteristics of machine learning will overwhelm existing data governance approaches such as privacy regulation and informed consent. Enhanced governance techniques and tools will be required to help preserve the autonomy and rights of individuals to control their PHI. Debate among all stakeholders and informed critique of how, and for whom, PHI-fueled health AI are developed and deployed are needed to channel these innovations in societally beneficial directions.Social implicationsHealth data may be used to address pressing societal concerns, such as operational and system-level improvement, and innovations such as personalized medicine. This paper informs work seeking to harness these resources for societal good amidst many competing value claims and substantial risks for privacy and security.Originality/valueThis is the first paper focusing on health data governance in relation to AI/machine learning.

Journal

Digital Policy, Regulation and Governance – Emerald Publishing

Published: Jul 17, 2019

Keywords: Big data; Governance; Artificial intelligence; Deep learning; Personal health information

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