Large language models (LLMs) are increasingly used in decision-making tasks where they can amplify or suppress perspectives, raising concerns in high-stakes settings affecting autistic communities. While previous research has identified disability-related biases in LLMs, it remains unclear how they conceptualize ableism or detect it in text. We introduce a bias-aware evaluation framework targeting anti-autistic ableist language with a psychometrically-weighted, community-proximate ground truth anchored in annotator positionality. This framework constitutes a stricter standard than conventional majority-vote aggregation which significantly and consistently underweights autistic and autism-accepting perspectives. We find that LLMs frequently produce harmful outputs, mislabel community-reclaimed language as ableist, and express more negative attitudes toward autistic people when assessment instruments are masked. Our error analysis reveals that models rely on surface-level keyword matching rather than contextual factors such as speaker identity, and whether the language fosters in-group solidarity or inflicts out-group harm.
@article{arxiv.2605.26397,
title = {Annotator Positionality as Signal: Psychometric Weighting for Anti-Autistic Ableism Detection},
author = {Naba Rizvi and Harper Strickland and Saleha Ahmedi and Nedjma Ousidhoum},
journal= {arXiv preprint arXiv:2605.26397},
year = {2026}
}