Intersectional Fairness in Large Language Models

Large Language Models (LLMs) are increasingly deployed in socially sensitive settings, raising concerns about fairness and bias, particularly when multiple sensitive attributes intersect. We systematically evaluate intersectional fairness in six LLMs using two datasets from the Bias Benchmark for Question Answering (BBQ), combining race with gender and socioeconomic status. We assess bias, subgroup fairness, accuracy, and consistency across contexts and question polarities. While models perform well in ambiguous contexts, sparse non-unknown predictions limit fairness evaluation. In disambiguated contexts, stereotype alignment affects accuracy differently across datasets: models favor ster...

<iframe src="https://silytics.si/embed/item/bee2e556-8854-4d35-a250-e64c6e38cc59" width="420" height="190" style="border:0" loading="lazy" title="Silytics entry"></iframe>

Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.