Governance of artificial intelligence and personal health information
| Autor: | Elizabeth J. Davidson, Jenifer Sunrise Winter |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Value (ethics)
Information Systems and Management Computer Networks and Communications business.industry Corporate governance media_common.quotation_subject 05 social sciences Big data Equity (finance) Management Information Systems Data governance 03 medical and health sciences 0302 clinical medicine Management of Technology and Innovation 0502 economics and business Health care 030212 general & internal medicine Justice (ethics) Artificial intelligence business 050203 business & management Autonomy Information Systems media_common |
| Zdroj: | Digital Policy, Regulation and Governance. 21:280-290 |
| ISSN: | 2398-5038 |
| DOI: | 10.1108/dprg-08-2018-0048 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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