Integration of fairness-awareness into clinical language processing models

Abstract

This study demonstrates that fairness can be integrated into clinical language models, though effects vary by model type. Architectures aligned with clinical text structure inherently promote fairness, yet mixed fairness constraint outcomes highlight the need for tailored interventions. Persistent demographic disparities show that algorithmic bias often reflects upstream documentation inequities. This framework offers a scalable path toward equitable NLP for clinical artificial intelligence.

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