Trading off accuracy and explainability in AI decision-making: findings from 2 citizens' juries.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 34333646

Abstract

OBJECTIVE: To investigate how the general public trades off explainability versus accuracy of artificial intelligence (AI) systems and whether this differs between healthcare and non-healthcare scenarios.

Authors

  • Sabine N van der Veer
    Centre for Health Informatics, Division of Informatics, Imaging and Data Science, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
  • Lisa Riste
    NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
  • Sudeh Cheraghi-Sohi
    NIHR Greater Manchester Patient Safety Translational Research Centre, School of Health Sciences, Manchester Academic Health Science Centre, The University of Manchester, Manchester, UK.
  • Denham L Phipps
    Division of Pharmacy and Optometry, School of Health Sciences, The University of Manchester, Manchester, UK.
  • Mary P Tully
    Division of Pharmacy and Optometry, School of Health Sciences, The University of Manchester, Manchester, UK.
  • Kyle Bozentko
    Jefferson Center, Saint Paul, Minnesota, USA.
  • Sarah Atwood
    Jefferson Center, Saint Paul, Minnesota, USA.
  • Alex Hubbard
    Information Commissioner's Office, Wilmslow, UK.
  • Carl Wiper
    Information Commissioner's Office, Wilmslow, UK.
  • Malcolm Oswald
    School of Law, Faculty of Humanities, The University of Manchester, Manchester, UK.
  • Niels Peek
    Health e-Research Centre, University of Manchester, Vaughan House, Portsmouth Street, Manchester M13 9GB, UK. Electronic address: niels.peek@manchester.ac.uk.

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