The Misclassification of Autistic Writing as AI-Generated

2026-07-16

AI Summary

This study addresses potential systematic bias in current AI-generated text detectors against autistic individuals. Leveraging a dataset of approximately 60,000 Reddit posts, the authors compare classification outcomes from the OpenAI GPT-2 detector between texts authored by individuals with autistic traits and those by neurotypical users, complemented by an analysis of linguistic features to elucidate underlying mechanisms. The work presents the first empirical evidence that, despite an overall false-positive rate below 2%, texts from autistic authors are significantly more likely to be misclassified as AI-generated. This finding reveals a fairness deficit in widely used detection tools when applied to neurodiverse populations and underscores the urgent need for critical ethical reflection on their deployment.

Abstract

Recent findings suggest that detection models for artificial intelligence (AI) cannot accurately identify AI-generated text and may exhibit bias against certain minority groups. In the present study, anecdotal claims that autistic writers more often have their work flagged as AI-generated are examined empirically. A corpus of approximately 60,000 Reddit posts split into "likely-autistic" and "general-Reddit" subcorpora is used to compare the distribution of probabilities output by the OpenAI GPT-2 detection model. Differences in textual features between subcorpora are observed and compared to reported features of AI-generated text. Results showed that while less than two-percent of either subcorpus was flagged as AI-generated by the model, significantly more texts from the likely-autistic subcorpus were flagged. Connections between features of text with likely-autistic authors and AI-generated text were not straightforward. The widespread use of AI-detection models with a potential bias against autistic writers in their output prompts ethical scrutiny, and the authors recommend further critical examination of the models themselves as well as their use in academic contexts.

Problem

• AI detection bias
• autistic writing
• AI-generated text
• algorithmic fairness
• text classification

Innovation

• AI detection bias
• autistic writing
• text classification fairness
• GPT-2 detector
• algorithmic discrimination