TY  - JOUR
AB  - Purpose 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/approach This 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.Findings This 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 implications Health 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/value This is the first paper focusing on health data governance in relation to AI/machine learning.
VL  - 21
IS  - 3
SN  - 2398-5038
DO  - 10.1108/DPRG-08-2018-0048
UR  - https://doi.org/10.1108/DPRG-08-2018-0048
AU  - Winter Jenifer Sunrise
AU  - Davidson Elizabeth
PY  - 2019
Y1  - 2019/01/01
TI  - Governance of artificial intelligence and personal health information
T2  - Digital Policy, Regulation and Governance
PB  - Emerald Publishing Limited
SP  - 280
EP  - 290
Y2  - 2024/04/25
ER  -