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

TL;DR

This study investigates how large language models respond to autistic users disclosing their autism, revealing a tendency to reinforce stereotypes and highlighting the complex effects on personalized advice.

Contribution

It introduces a novel pipeline to operationalize autism stereotypes in LLM decision scenarios and analyzes their impact on advice given to autistic users.

Findings

LLMs tend to recommend avoiding stressful situations when autism is disclosed.

Disclosed autism can lead to stereotypical and infantilizing advice.

Participants have mixed perceptions of stereotype reinforcement in advice.

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.