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Teaching yourself about structural racism will improve your machine learning.

Whitney R Robinson1, Audrey Renson2, Ashley I Naimi3

  • 1Department of Epidemiology, UNC Gillings School of Global Public Health, University of North Carolina at Chapel Hill; Carolina Population Center, University of North Carolina at Chapel Hill, CB #7345 McGavran-Greenberg, Chapel Hill, NC 27599-7435, USA.

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This summary is machine-generated.

Machine learning in health requires understanding structural racism. This knowledge is essential for ensuring health research models are applicable across diverse populations.

Keywords:
Causal inferenceDirected acyclic graphsMachine learningStructural racism

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Area of Science:

  • Health Research
  • Machine Learning
  • Social Determinants of Health

Background:

  • Machine learning is increasingly used in health research.
  • Generalizability of health research models is a persistent challenge.
  • Structural racism significantly impacts health outcomes and disparities.

Purpose of the Study:

  • To argue for the necessity of understanding structural racism in health-related machine learning.
  • To highlight the role of structural racism in achieving generalizability in health research.

Main Methods:

  • This is a commentary, not an empirical study.
  • The argument is based on existing literature and theoretical frameworks regarding structural racism and machine learning in health.

Main Results:

  • Structural racism is a critical factor influencing health data.
  • Ignoring structural racism can lead to biased and non-generalizable machine learning models in health.

Conclusions:

  • Educating machine learning practitioners on structural racism is imperative.
  • Integrating knowledge of structural racism is crucial for equitable and effective health research.