March 14, 2024, 4:48 a.m. | Xingmeng Zhao, Ali Niazi, Anthony Rios

cs.CL updates on arXiv.org arxiv.org

arXiv:2212.12799v2 Announce Type: replace
Abstract: Chemical named entity recognition (NER) models are used in many downstream tasks, from adverse drug reaction identification to pharmacoepidemiology. However, it is unknown whether these models work the same for everyone. Performance disparities can potentially cause harm rather than the intended good. This paper assesses gender-related performance disparities in chemical NER systems. We develop a framework for measuring gender bias in chemical NER models using synthetic data and a newly annotated corpus of over 92,405 …

abstract arxiv bias cs.cl gender gender bias good harm however identification ner performance recognition study tasks type work

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