Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism

TL;DR

This paper assesses how well large language models can detect nuanced ableism in text, revealing their reliance on keywords and highlighting the importance of context for accurate identification.

Contribution

It provides a detailed evaluation of LLMs' ability to recognize nuanced ableism, comparing their performance to human judgment and analyzing their interpretative limitations.

Findings

LLMs can identify autism-related language but often miss harmful connotations.

LLMs rely heavily on keyword matching, leading to context misinterpretations.

Both LLMs and humans agree on a binary classification scheme for ableism detection.

Abstract

Large language models (LLMs) are increasingly used in decision-making tasks like r\'esum\'e screening and content moderation, giving them the power to amplify or suppress certain perspectives. While previous research has identified disability-related biases in LLMs, little is known about how they conceptualize ableism or detect it in text. We evaluate the ability of four LLMs to identify nuanced ableism directed at autistic individuals. We examine the gap between their understanding of relevant terminology and their effectiveness in recognizing ableist content in context. Our results reveal that LLMs can identify autism-related language but often miss harmful or offensive connotations. Further, we conduct a qualitative comparison of human and LLM explanations. We find that LLMs tend to rely on surface-level keyword matching, leading to context misinterpretations, in contrast to human annotators who consider context, speaker identity, and potential impact. On the other hand, both LLMs and humans agree on the annotation scheme, suggesting that a binary classification is adequate for evaluating LLM performance, which is consistent with findings from prior studies involving human annotators.