2026-04-16 バージニア工科大学（Virginia Tech）
Caleb Wohn, a computer science doctoral student, is the lead author on a project that raises difficult questions about whether commercial AI systems are personalizing their responses for users who disclose autism diagnoses, or if it’s giving biased advice that reinforces stereotypes. Photo by Tonia Moxley for Virginia Tech.
＜関連情報＞
- https://news.vt.edu/articles/2026/04/eng-cs-autism-AI-advice-personalization-or-bias.html
- https://arxiv.org/abs/2601.12690
「私たちはここでスポックへのアドバイスコラムを書いているのか？」自閉症ユーザー向けAIアドバイスにおけるステレオタイプの理解 “Are we writing an advice column for Spock here?” Understanding Stereotypes in AI Advice for Autistic Users
Caleb Wohn, Buse Çarık, Xiaohan Ding, Sang Won Lee, Young-Ho Kim, Eugenia H. Rho
arXiv Submitted on 19 Jan 2026
DOI:https://doi.org/10.48550/arXiv.2601.12690
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.
