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arXiv Machine Learning By Carter H. Nakamoto, Lucia Lushi Chen, Agata Foryciarz, Sherri Rose

Penalized Fair Regression for Multiple Groups in Chronic Kidney Disease

Read the original on arXiv Machine Learning →

The paper introduces a penalized fair regression framework that incorporates unfairness penalties for multiple groups, specifically targeting binary outcomes with true positive rate disparity penalties. It can be efficiently implemented via a reduction to cost‑sensitive classification and includes new score functions for selecting penalty weights automatically. Empirical studies show that the method achieves a better fairness‑accuracy trade‑off than existing approaches, and when applied to a national chronic kidney disease dataset it markedly improves fairness across race and ethnicity groups without significantly harming overall model fit.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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