Identifying and mitigating algorithmic bias in the safety net
Abstract Algorithmic bias occurs when predictive model performance varies meaningfully across sociodemographic classes, exacerbating systemic healthcare disparities. NYC Health + Hospitals, an urban safety net system, assessed bias in two binary classification models in our electronic medical record...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-06-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01732-w |
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| Summary: | Abstract Algorithmic bias occurs when predictive model performance varies meaningfully across sociodemographic classes, exacerbating systemic healthcare disparities. NYC Health + Hospitals, an urban safety net system, assessed bias in two binary classification models in our electronic medical record: one predicting acute visits for asthma and one predicting unplanned readmissions. We evaluated differences in subgroup performance across race/ethnicity, sex, language, and insurance using equal opportunity difference (EOD), a metric comparing false negative rates. The most biased classes (race/ethnicity for asthma, insurance for readmission) were targeted for mitigation using threshold adjustment, which adjusts subgroup thresholds to minimize EOD, and reject option classification, which re-classifies scores near the threshold by subgroup. Successful mitigation was defined as 1) absolute subgroup EODs <5 percentage points, 2) accuracy reduction <10%, and 3) alert rate change <20%. Threshold adjustment met these criteria; reject option classification did not. We introduce a Supplementary Playbook outlining our approach for low-resource bias mitigation. |
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| ISSN: | 2398-6352 |