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Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
2
Zitationen
3
Autoren
2022
Jahr
Abstract
in certain groups, and in turn, more biased labels for such groups. Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups. Here, we theoretically and empirically characterize conditions in which disparate censorship or undertesting affect model performance across subgroups. Our findings call attention to disparate censorship as a source of label bias in clinical ML models.
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