Beyond Keywords: Evaluating Large Language Model Classification of Nuanced Ableism
Journal:
arXiv
Published Date:
May 26, 2025
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.
