A scoping review of fair machine learning techniques when using real-world data.

Journal: Journal of biomedical informatics
Published Date: Mar 6, 2024

Abstract

OBJECTIVE: The integration of artificial intelligence (AI) and machine learning (ML) in health care to aid clinical decisions is widespread. However, as AI and ML take important roles in health care, there are concerns about AI and ML associated fairness and bias. That is, an AI tool may have a disparate impact, with its benefits and drawbacks unevenly distributed across societal strata and subpopulations, potentially exacerbating existing health inequities. Thus, the objectives of this scoping review were to summarize existing literature and identify gaps in the topic of tackling algorithmic bias and optimizing fairness in AI/ML models using real-world data (RWD) in health care domains.

Authors

  • Yu Huang
    School of Data Science and Software Engineering, Qingdao University, Qingdao 266021, China.
  • Jingchuan Guo
    Department of Pharmaceutical Outcomes and Policy, University of Florida, Gainesville, FL.
  • Wei-Han Chen
    Department of Athletic Performance, National Taiwan Normal University, Taipei, Taiwan.
  • Hsin-Yueh Lin
    Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • Huilin Tang
    Pharmaceutical Outcomes & Policy, University of Florida, Gainesville, FL, USA.
  • Fei Wang
    Department of Population Health Sciences, Weill Cornell Medicine, Cornell University, New York, NY, United States.
  • Hua Xu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jiang Bian
    Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, United States of America.

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