Poster
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Datasets and Benchmarks: Dataset and Benchmark Poster Session 3

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

Cécile Logé · Emily Ross · David Dadey · Saahil Jain · Adriel Saporta · Andrew 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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