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Recent advances in Natural Language Processing (NLP), and specificallyautomated Question Answering (QA) systems, have demonstrated both impressivelinguistic fluency and a pernicious tendency to reflect social biases. In thisstudy, we introduce Q-Pain, a dataset for assessing bias in medical QA in thecontext of pain management, one of the most challenging forms of clinicaldecision-making. Along with the dataset, we propose a new, rigorous framework,including a sample experimental design, to measure the potential biases presentwhen making treatment decisions. We demonstrate its use by assessing tworeference Question-Answering systems, GPT-2 and GPT-3, and find statisticallysignificant differences in treatment between intersectional race-gendersubgroups, thus reaffirming the risks posed by AI in medical settings, and theneed for datasets like ours to ensure safety before medical AI applications aredeployed.

Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management