ArticleBMC medical informatics and decision making2025
Benchmarking bias in embeddings of healthcare AI models: using SD-WEAT for detection and measurement across sensitive populations.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundArtificial intelligence (AI) has been shown to exhibit and perpetuate human biases; recent research efforts have focused on measuring bias within the input embeddings of AI language models, especially with non-binary classifications that are common in medicine and healthcare scenarios. For instance, ethnicity-linked terms might include categories such as Asian, Black, Hispanic, and White, complicating the definition of – traditionally binary – attribute groups. In this study, we aimed to develop a new framework to detect and measure inherent medical biases based on SD-WEAT (Standard Deviation - Word Embedding Association Test). Compared to its predecessor, WEAT, SD-WEAT was able to measure bias among multi-level attribute groups common in the field of medicine, such as age, race, and region.
methodsWe constructed a collection of medicine-based benchmarks that can be used to detect and measure biases among sex, ethnicities, and medical conditions. Then, we evaluated a collection of language models, including GloVe, BERT, LegalBERT, BioBERT, GPT-2, and BioGPT, and determined which had potential undesirable or desirable healthcare biases.
resultsWith the presented framework, we were able to detect and measure a significant presence of bias among gender-linked (P < 0.01) and ethnicity-linked (P < 0.01) medical conditions for a biomedicine-focused language model (e.g., BioBERT) compared to general BERT models. In addition, we demonstrated that SD-WEAT was capable of simultaneously handling multiple attribute groups, detecting and measuring bias among a collection of ethnicity-linked medical conditions and multiple ethnic/racial groups.
conclusionsTo conclude, we presented an AI bias measurement framework, based on SD-WEAT. This framework provided a promising approach to detect and measure biases in language models that have been applied in biomedical/healthcare text analysis.
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