Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science.

Journal: Clinical pharmacology and therapeutics
Published Date: Dec 12, 2023

Abstract

Artificial intelligence (AI) is increasingly being used in decision making across various industries, including the public health arena. Bias in any decision-making process can significantly skew outcomes, and AI systems have been shown to exhibit biases at times. The potential for AI systems to perpetuate and even amplify biases is a growing concern. Bias, as used in this paper, refers to the tendency toward a particular characteristic or behavior, and thus, a biased AI system is one that shows biased associations entities. In this literature review, we examine the current state of research on AI bias, including its sources, as well as the methods for measuring, benchmarking, and mitigating it. We also examine the biases and methods of mitigation specifically relevant to the healthcare field and offer a perspective on bias measurement and mitigation in regulatory science decision making.

Authors

  • Magnus Gray
    Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, United States Food and Drug Administration, 3900 NCTR Road, Jefferson, Arkansas 72079 United States.
  • Ravi Samala
    Department of Radiology, University of Michigan, Ann Arbor, MI, 48109, USA.
  • Qi Liu
    National Institute of Traditional Chinese Medicine Constitution and Preventive Medicine, Beijing University of Chinese Medicine, Beijing, China.
  • Denny Skiles
    Office of Management, National Center for Toxicological Research, US Food and Drug Administration, Jefferson, Arkansas, USA.
  • Joshua Xu
    Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA.
  • Weida Tong
    National Center for Toxicological Research, Division of Bioinformatics and Biostatistics, U.S. Food and Drug Administration, Jefferson, AR, United States.
  • Leihong Wu
    Division of Bioinformatics and Biostatistics, National Center for Toxicological Research, U.S. Food and Drug Administration, 3900 NCTR Rd, Jefferson, AR, 72079, USA. Leihong.wu@fda.hhs.gov.

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