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Leveraging clinical epidemiology concepts to strengthen machine learning fairness evaluations
0
Zitationen
7
Autoren
2026
Jahr
Abstract
Many parallels exist between ML fairness and clinical epidemiology, including the conceptualization of the root causes of unfairness, the articulation of fairness criteria, and considerations related to multiple testing. Methodologically sound fairness approaches can leverage well-established principles from clinical epidemiology.
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