Defining AMIA's artificial intelligence principles

Authors

Anthony E. Solomonides, NorthShore University HealthSystem, Research Institute, Evanston, Illinois, USA.
Eileen Koski, Center for Computational Health, IBM T. J. Watson Research Center, Yorktown Heights, New York, USA.
Shireen M. Atabaki, Pediatrics; Emergency Medicine, The George Washington University School of Medicine Children s National Hospital, Washington, District of Columbia, USA.
Scott Weinberg, Public Policy, American Medical Informatics Association, Rockville, Maryland, USA.
John D. McGreevey, Center for Applied Health Informatics and Office of the Chief Medical Information Officer, University of Pennsylvania Health System, Philadelphia, Pennsylvania, USA.
Joseph L. Kannry, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
Carolyn Petersen, Health Education & Content Services, Mayo Clinic, Rochester, Minnesota, USA.
Christoph U. Lehmann, Clinical Informatics Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA.

Document Type

Journal Article

Publication Date

3-15-2022

Journal

Journal of the American Medical Informatics Association : JAMIA

Volume

29

Issue

4

DOI

10.1093/jamia/ocac006

Keywords

Belmont principles; artificial intelligence; bias; ethical principles; machine learning; patient-centered; transparency; trustworthiness

Abstract

Recent advances in the science and technology of artificial intelligence (AI) and growing numbers of deployed AI systems in healthcare and other services have called attention to the need for ethical principles and governance. We define and provide a rationale for principles that should guide the commission, creation, implementation, maintenance, and retirement of AI systems as a foundation for governance throughout the lifecycle. Some principles are derived from the familiar requirements of practice and research in medicine and healthcare: beneficence, nonmaleficence, autonomy, and justice come first. A set of principles follow from the creation and engineering of AI systems: explainability of the technology in plain terms; interpretability, that is, plausible reasoning for decisions; fairness and absence of bias; dependability, including "safe failure"; provision of an audit trail for decisions; and active management of the knowledge base to remain up to date and sensitive to any changes in the environment. In organizational terms, the principles require benevolence-aiming to do good through the use of AI; transparency, ensuring that all assumptions and potential conflicts of interest are declared; and accountability, including active oversight of AI systems and management of any risks that may arise. Particular attention is drawn to the case of vulnerable populations, where extreme care must be exercised. Finally, the principles emphasize the need for user education at all levels of engagement with AI and for continuing research into AI and its biomedical and healthcare applications.

APA Citation

Solomonides, Anthony E.; Koski, Eileen; Atabaki, Shireen M.; Weinberg, Scott; McGreevey, John D.; Kannry, Joseph L.; Petersen, Carolyn; and Lehmann, Christoph U., "Defining AMIA's artificial intelligence principles" (2022). GW Authored Works. Paper 591.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/591

Department

Pediatrics