Hurtful words - quantifying biases in clinical contextual word embeddings
ACM CHIL '20: ACM Conference on Health, Inference, and Learning Toronto Ontario Canada April, 2020, pp. 110-120, 2020.
In this work, we examine the extent to which embeddings may encode marginalized populations differently, and how this may lead to a perpetuation of biases and worsened performance on clinical tasks. We pretrain deep embedding models (BERT) on medical notes from the MIMIC-III hospital dataset, and quantify potential disparities using two a...More
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