Public articles linked to the same research event.
arXiv Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).
Using MedRAX as the instantiated system, this work audits fairness in tool-using chest X-ray question-answering agents and proposes DUCX, a stage-wise decomposition that separates end-to-end bias into tool-exposure bias, tool-transition bias, and LLM reasoning bias; across five driver LLMs on CheXAgentBench and the curated MIMIC-FairnessVQA, demographic gaps persist end to end (equalized odds up to 20.79%, lowest fairness-utility tradeoff down to 28.65%), and subgroup disparities in tool usage, routing patterns, and reasoning traces are not predictable from end-to-end evaluation alone (for example, conditioned on segmentation-tool availability the subgroup utility gap reaches as high as 50%).