Toward Deconfounding the Influence of Entity Demographics for Question Answering Accuracy
Abstract
Question answering models show consistent accuracy across genders and nationalities but exhibit higher variability based on profession, indicating a need for more diverse datasets.
The goal of question answering (QA) is to answer any question. However, major QA datasets have skewed distributions over gender, profession, and nationality. Despite that skew, model accuracy analysis reveals little evidence that accuracy is lower for people based on gender or nationality; instead, there is more variation on professions (question topic). But QA's lack of representation could itself hide evidence of bias, necessitating QA datasets that better represent global diversity.
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