Population Preferences for Performance and Explainability of Artificial Intelligence in Health Care: Choice-Based Conjoint Survey.

Journal: Journal of medical Internet research
Published Date:

Abstract

BACKGROUND: Certain types of artificial intelligence (AI), that is, deep learning models, can outperform health care professionals in particular domains. Such models hold considerable promise for improved diagnostics, treatment, and prevention, as well as more cost-efficient health care. They are, however, opaque in the sense that their exact reasoning cannot be fully explicated. Different stakeholders have emphasized the importance of the transparency/explainability of AI decision making. Transparency/explainability may come at the cost of performance. There is need for a public policy regulating the use of AI in health care that balances the societal interests in high performance as well as in transparency/explainability. A public policy should consider the wider public's interests in such features of AI.

Authors

  • Thomas Ploug
    Department of Communication and Psychology, Centre of Applied Ethics and Philosophy of Science, Aalborg University, Copenhagen, Denmark.
  • Anna Sundby
    Department of Communication and Psychology, Aalborg University, Copenhagen, Denmark.
  • Thomas B Moeslund
    Visual Analysis and Perception Lab, Aalborg University, Aalborg, Denmark.
  • Søren Holm
    Centre for Social Ethics and Policy, School of Law, University of Manchester, Manchester, United Kingdom.