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Electronic health records (EHRs) serve as an essential data source for theenvisioned artificial intelligence (AI)-driven transformation in healthcare.However, clinician biases reflected in EHR notes can lead to AI modelsinheriting and amplifying these biases, perpetuating health disparities. Thisstudy investigates the impact of stigmatizing language (SL) in EHR notes onmortality prediction using a Transformer-based deep learning model andexplainable AI (XAI) techniques. Our findings demonstrate that SL written byclinicians adversely affects AI performance, particularly so for blackpatients, highlighting SL as a source of racial disparity in AI modeldevelopment. To explore an operationally efficient way to mitigate SL's impact,we investigate patterns in the generation of SL through a clinicians'collaborative network, identifying central clinicians as having a strongerimpact on racial disparity in the AI model. We find that removing SL written bycentral clinicians is a more efficient bias reduction strategy than eliminatingall SL in the entire corpus of data. This study provides actionable insightsfor responsible AI development and contributes to understanding clinicianbehavior and EHR note writing in healthcare.

Echoes of Biases: How Stigmatizing Language Affects AI Performance