Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution

TL;DR

This paper introduces a new benchmark and metric to evaluate intersectional bias in large language models, revealing significant confidence disparities across multiple demographic intersections and highlighting concerns about model fairness and reasoning.

Contribution

It extends bias evaluation to intersectional identities, creates the WinoIdentity benchmark, and proposes the Coreference Confidence Disparity metric for assessing fairness in LLMs.

Findings

Confidence disparities up to 40% across demographic attributes.

Models are most uncertain about doubly-disadvantaged identities.

Confidence decreases even for privileged markers, indicating memorization over reasoning.

Abstract

Large language models (LLMs) have achieved impressive performance, leading to their widespread adoption as decision-support tools in resource-constrained contexts like hiring and admissions. There is, however, scientific consensus that AI systems can reflect and exacerbate societal biases, raising concerns about identity-based harm when used in critical social contexts. Prior work has laid a solid foundation for assessing bias in LLMs by evaluating demographic disparities in different language reasoning tasks. In this work, we extend single-axis fairness evaluations to examine intersectional bias, recognizing that when multiple axes of discrimination intersect, they create distinct patterns of disadvantage. We create a new benchmark called WinoIdentity by augmenting the WinoBias dataset with 25 demographic markers across 10 attributes, including age, nationality, and race, intersected with binary gender, yielding 245,700 prompts to evaluate 50 distinct bias patterns. Focusing on harms of omission due to underrepresentation, we investigate bias through the lens of uncertainty and propose a group (un)fairness metric called Coreference Confidence Disparity which measures whether models are more or less confident for some intersectional identities than others. We evaluate five recently published LLMs and find confidence disparities as high as 40% along various demographic attributes including body type, sexual orientation and socio-economic status, with models being most uncertain about doubly-disadvantaged identities in anti-stereotypical settings. Surprisingly, coreference confidence decreases even for hegemonic or privileged markers, indicating that the recent impressive performance of LLMs is more likely due to memorization than logical reasoning. Notably, these are two independent failures in value alignment and validity that can compound to cause social harm.