Racial Bias in Clinical and Population Health Algorithms: A Critical Review of Current Debates.

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.

Designing equitable algorithms.

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.