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State-of-the-art question answering (QA) models exhibit a variety of socialbiases (e.g., with respect to sex or race), generally explained by similarissues in their training data. However, what has been overlooked so far is thatin the critical domain of biomedicine, any unjustified change in model outputdue to patient demographics is problematic: it results in the unfair treatmentof patients. Selecting only questions on biomedical topics whose answers do notdepend on ethnicity, sex, or sexual orientation, we ask the following researchquestions: (RQ1) Do the answers of QA models change when being provided withirrelevant demographic information? (RQ2) Does the answer of RQ1 differ betweenknowledge graph (KG)-grounded and text-based QA systems? We find thatirrelevant demographic information change up to 15% of the answers of aKG-grounded system and up to 23% of the answers of a text-based system,including changes that affect accuracy. We conclude that unjustified answerchanges caused by patient demographics are a frequent phenomenon, which raisesfairness concerns and should be paid more attention to.

Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems