Identifying, Explaining, and Correcting Ableist Language with AI

TL;DR

This study explores how large language models like ChatGPT can identify, explain, and correct ableist language, comparing AI and human annotations, and providing insights into AI's role in promoting inclusive communication.

Contribution

It introduces a dataset of ableism annotations and offers design considerations for AI tools aimed at fostering inclusive language use.

Findings

Participants agreed equally with AI and human annotations

Participants preferred AI annotations for narrative consistency

Humans provided more emotional and cultural context

Abstract

Ableist language perpetuates harmful stereotypes and exclusion, yet its nuanced nature makes it difficult to recognize and address. Artificial intelligence could serve as a powerful ally in the fight against ableist language, offering tools that detect and suggest alternatives to biased terms. This two-part study investigates the potential of large language models (LLMs), specifically ChatGPT, to rectify ableist language and educate users about inclusive communication. We compared GPT-4o generations with crowdsourced annotations from trained disability community members, then invited disabled participants to evaluate both. Participants reported equal agreement with human and AI annotations but significantly preferred the AI, citing its narrative consistency and accessible style. At the same time, they valued the emotional depth and cultural grounding of human annotations. These findings highlight the promise and limits of LLMs in handling culturally sensitive content. Our contributions include a dataset of nuanced ableism annotations and design considerations for inclusive writing tools.