ArticleAutism research : official journal of the International Society for Autism Research2024
Prevalence of bias against neurodivergence-related terms in artificial intelligence language models.
Article in Autism research : official journal of the International Society for Autism Research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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Who cites it
8 citing papers in PubMed.
- Contribution of artificial intelligence research to the development of inclusive applications for people with disabilities: A scoping review (2015-2025).PLOS digital health · 2026Article
- Mapping the role of artificial intelligence in health-related stigma: a scoping review.NPJ digital medicine · 2026Article
- Digital Mental Health Through an Intersectional Lens: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- You must not fool yourself: Feynman, neurodiversity, and honest AI in digital mental health.Frontiers in digital health · 2026Article
- Practicing pharmacist education based on the experiences of medical support on the cruise ship Diamond Princess.BMC medical education · 2025Article
- Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review.Current treatment options in psychiatry · 2025Review
- Autoethnographic Insights from Neurodivergent GAI "Power Users".Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Given the increasing role of artificial intelligence (AI) in many decision-making processes, we investigate the presence of AI bias towards terms related to a range of neurodivergent conditions, including autism, ADHD, schizophrenia, and obsessive-compulsive disorder (OCD). We use 11 different language model encoders to test the degree to which words related to neurodiversity are associated with groups of words related to danger, disease, badness, and other negative concepts. For each group of words tested, we report the mean strength of association (Word Embedding Association Test [WEAT] score) averaged over all encoders and find generally high levels of bias. Additionally, we show that bias occurs even when testing words associated with autistic or neurodivergent strengths. For example, embedders had a negative average association between words related to autism and words related to honesty, despite honesty being considered a common strength of autistic individuals. Finally, we introduce a sentence similarity ratio test and demonstrate that many sentences describing types of disabilities, for example, "I have autism" or "I have epilepsy," have even stronger negative associations than control sentences such as "I am a bank robber."
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.