Annu Rev Public Health
December 2024
Among health care researchers, there is increasing debate over how best to assess and ensure the fairness of algorithms used for clinical decision support and population health, particularly concerning potential racial bias. Here we first distill concerns over the fairness of health care algorithms into four broad categories: () the explicit inclusion (or, conversely, the exclusion) of race and ethnicity in algorithms, () unequal algorithm decision rates across groups, () unequal error rates across groups, and () potential bias in the target variable used in prediction. With this taxonomy, we critically examine seven prominent and controversial health care algorithms.
View Article and Find Full Text PDFBackground: Accounting for race and ethnicity in estimating disease risk may improve the accuracy of predictions but may also encourage a racialized view of medicine.
Objective: To present a decision analytic framework for considering the potential benefits of race-aware over race-unaware risk predictions, using cardiovascular disease, breast cancer, and lung cancer as case studies.
Design: Cross-sectional study.
Predictive algorithms are now commonly used to distribute society's resources and sanctions. But these algorithms can entrench and exacerbate inequities. To guard against this possibility, many have suggested that algorithms be subject to formal fairness constraints.
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