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

arXiv: https://arxiv.org/abs/2108.01764 · HTML: https://mlsanity.com/paper/2108.01764/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 40 references that are in the map.
- First posted on arXiv 3 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [StereoSet: Measuring stereotypical bias in pretrained language models](https://mlsanity.com/paper/2004.09456/) (2020)
- [CrowS-Pairs: A Challenge Dataset for Measuring Social Biases in Masked Language Models](https://mlsanity.com/paper/2010.00133/) (2020)
- [The Woman Worked as a Babysitter: On Biases in Language Generation](https://mlsanity.com/paper/1909.01326/) (2019)
- [emrQA: A Large Corpus for Question Answering on Electronic Medical Records](https://mlsanity.com/paper/1809.00732/) (2018)
- [A Question-Entailment Approach to Question Answering](https://mlsanity.com/paper/1901.08079/) (2019)
- [UnQovering Stereotyping Biases via Underspecified Questions](https://mlsanity.com/paper/2010.02428/) (2020)
- [Towards Controllable Biases in Language Generation](https://mlsanity.com/paper/2005.00268/) (2020)
- [HEAD-QA: A Healthcare Dataset for Complex Reasoning](https://mlsanity.com/paper/1906.04701/) (2019)
- [Hurtful Words: Quantifying Biases in Clinical Contextual Word Embeddings](https://mlsanity.com/paper/2003.11515/) (2020)
- [The Radicalization Risks of GPT-3 and Advanced Neural Language Models](https://mlsanity.com/paper/2009.06807/) (2020)
- [Medical Exam Question Answering with Large-scale Reading Comprehension](https://mlsanity.com/paper/1802.10279/) (2018)
- [Is Your Classifier Actually Biased? Measuring Fairness under Uncertainty with Bernstein Bounds](https://mlsanity.com/paper/2004.12332/) (2020)

## Built on by (most cited first, 16 of 16)

- [BioClinical ModernBERT: A State-of-the-Art Long-Context Encoder for Biomedical and Clinical NLP](https://mlsanity.com/paper/2506.10896/) (2025)
- [Bias patterns in the application of LLMs for clinical decision support: A comprehensive study](https://mlsanity.com/paper/2404.15149/) (2024)
- [AfriMed-QA: A Pan-African, Multi-Specialty, Medical Question-Answering Benchmark Dataset](https://mlsanity.com/paper/2411.15640/) (2024)
- [Data Representativity for Machine Learning and AI Systems](https://mlsanity.com/paper/2203.04706/) (2022)
- [a survey on GPT-3](https://mlsanity.com/paper/2212.00857/) (2022)
- [LExT: Towards Evaluating Trustworthiness of Natural Language Explanations](https://mlsanity.com/paper/2504.06227/) (2025)
- [Implicit Bias in LLMs: A Survey](https://mlsanity.com/paper/2503.02776/) (2025)
- [Aligning (Medical) LLMs for (Counterfactual) Fairness](https://mlsanity.com/paper/2408.12055/) (2024)
- [What Do You See in this Patient? Behavioral Testing of Clinical NLP Models](https://mlsanity.com/paper/2111.15512/) (2021)
- [Enabling Scalable Evaluation of Bias Patterns in Medical LLMs](https://mlsanity.com/paper/2410.14763/) (2024)
- [Compared to What? Baselines and Metrics for Counterfactual Prompting](https://mlsanity.com/paper/2605.01048/) (2026)
- [Evaluating Biases in Context-Dependent Health Questions](https://mlsanity.com/paper/2403.04858/) (2024)
- [Building Trust in Clinical LLMs: Bias Analysis and Dataset Transparency](https://mlsanity.com/paper/2510.18556/) (2025)
- [mFARM: Towards Multi-Faceted Fairness Assessment based on HARMs in Clinical Decision Support](https://mlsanity.com/paper/2509.02007/) (2025)
- [Can SAEs reveal and mitigate racial biases of LLMs in healthcare?](https://mlsanity.com/paper/2511.00177/) (2025)
- [Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems](https://mlsanity.com/paper/2310.10571/) (2023)

## Abstract (arXiv)

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.