Abstract Number:
3419
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Sarah Hegarty (1), Kristin Linn (1), Jinbo Chen (1)
Institutions:
(1) University of Pennsylvania, N/A
Co-Author(s):
First Author:
Presenting Author:
Abstract Text:
The proliferation of risk prediction models and algorithm-assisted decision making has spurred demands for methods to identify, quantify and reduce algorithm bias. One popular notion of fairness, equal opportunity, requires parity in true positive rate (TPR) across subgroups. While intuitively appealing, models constrained to satisfy the equal opportunity condition suffer from a loss in overall accuracy. Moreover, even models with perfect prediction can fail to satisfy equal opportunity if the risk distribution differs between subgroups. In the healthcare setting, the true risk distribution can be expected to differ between subgroups; therefore, fairness measures that account for these differences are needed. We investigate how TPR and related error-rate based metrics are expected to differ between subgroups in the presence of differences in underlying risk distribution and/or subgroup-dependent calibration bias through a combination of theoretical and numerical work. We further propose a modified formulation of the equal opportunity criterion and apply it to a risk prediction model implemented in a large, urban health system.
Keywords:
algorithm bias|fairness| equal opportunity|true positive rate|calibration bias|
Sponsors:
Health Policy Statistics Section
Tracks:
Miscellaneous
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