Work

Fair admission risk prediction with proportional multicalibration.

Year: 2023

Type: article

Abstract: , a fairness criteria that directly measures how closely a model approximates sufficiency. Therefore, proportionally calibrated models limit the ability of decision makers to distinguish between model performance on different patient groups, which may make the models more trustworthy in practice. We provide an efficient algorithm for post-processing risk prediction models for proportional multicalibration and evaluate it empirically. We conduct simulation studies and investigate a real-world application of PMC-postprocessing to prediction of emergency department patient admissions. We observe that proportional multicalibration is a promising criteria for controlling simultaneous measures of calibration fairness of a model over intersectional groups with virtually no cost in terms of classification performance.

Source: PubMed

Authors: William G La Cava, Elle Lett, Guangya Wan

Institutions: Boston Children's Hospital, Harvard University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute of Child Health and Human Development, U.S. National Library of Medicine, Eunice Kennedy Shriver National Institute of Child Health and Human Development