"Are we writing an advice column for Spock here?"Understanding Stereotypes in AI Advice for Autistic Users

2026-01-19

AI Summary

This study investigates the tension between personalized support and stereotyping risks when autistic users disclose their identity while seeking social advice from large language models (LLMs). For the first time, it systematically operationalizes 12 autism-related stereotypes into decision-making scenarios and employs a six-step analytical framework combining large-scale LLM auditing—spanning six models and generating 345,000 advice responses—with in-depth interviews of 11 autistic individuals. Findings reveal that identity disclosure leads models to disproportionately recommend avoiding social interaction, conflict, novel experiences, and intimate relationships. While some participants felt understood, others reported being infantilized or constrained by developmental assumptions, highlighting a complex interplay in AI-generated advice where affirming support coexists with stereotypical limitations.

Technology Category

• Humans and AI: AI for Accessibility
• Natural Language Processing: (Large) Language Models
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Autistic individuals sometimes disclose autism when asking LLMs for social advice, hoping for more personalized responses. However, they also recognize that these systems may reproduce stereotypes, raising uncertainty about the risks and benefits of disclosure. We conducted a mixed-methods study combining a large-scale LLM audit experiment with interviews involving 11 autistic participants. We developed a six-step pipeline operationalizing 12 documented autism stereotypes into decision-making scenarios framed as users requesting advice (e.g.,"Should I do A or B?"). We generated 345,000 responses from six LLMs and measured how advice shifted when prompts disclosed autism versus when they did not. When autism was disclosed, LLMs disproportionately recommended avoiding stereotypically stressful situations, including social events, confrontations, new experiences, and romantic relationships. While some participants viewed this as affirming, others criticized it as infantilizing or undermining opportunities for growth. Our study illuminates how the intermingling of affirmation and stereotyping complicates the personalization of LLMs.

Problem

• autism
• stereotypes
• large language models
• social advice
• personalization

Innovation

• stereotype operationalization
• LLM audit
• autism disclosure
• personalized advice
• mixed-methods evaluation