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arXiv · 2108.01764

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

Cécile Logé·Emily Ross·David Yaw Amoah Dadey·Saahil Jain·Adriel Saporta·Andrew Y. Ng·Pranav Rajpurkar
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Abstract

Recent advances in Natural Language Processing (NLP), and specifically automated Question Answering (QA) systems, have demonstrated both impressive linguistic fluency and a pernicious tendency to reflect social biases. In this study, we introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management, one of the most challenging forms of clinical decision-making. Along with the dataset, we propose a new, rigorous framework, including a sample experimental design, to measure the potential biases present when making treatment decisions. We demonstrate its use by assessing two reference Question-Answering systems, GPT-2 and GPT-3, and find statistically significant differences in treatment between intersectional race-gender subgroups, thus reaffirming the risks posed by AI in medical settings, and the need for datasets like ours to ensure safety before medical AI applications are deployed.

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Cécile Logé, Emily Ross, David Yaw Amoah Dadey, Saahil Jain, Adriel Saporta, Andrew Y. Ng, Pranav Rajpurkar. 2021-08-03. Q-Pain: A Question Answering Dataset to Measure Social Bias in Pain Management. https://arxiv.org/abs/2108.01764

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