Identifying, Explaining, and Correcting Ableist Language with AI
Kynnedy Simone Smith, Lydia B. Chilton, Danielle Bragg
摘要
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.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
它引用的顶会 Paper14
- To Trust or to Think: Cognitive Forcing Functions Can Reduce Overreliance on AI in AI-assisted Decision-makingZana Buçinca, Maja Barbara Malaya, Krzysztof Z. GajosCSCW 2021 · 被引用 962 次
- AI Chains: Transparent and Controllable Human-AI Interaction by Chaining Large Language Model PromptsTongshuang Wu, Michael Terry, Carrie Jun CaiCHI 2022 · 被引用 465 次
- Getting Ourselves Together: Data-centered participatory design research & epistemic burdenJennifer Pierre, Roderic N. Crooks, Morgan E. Currie, Britt S. Paris 等CHI 2021 · 被引用 112 次
- The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With RealityMitchell L. Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori Hashimoto 等CHI 2021 · 被引用 100 次
- A Systematic Review and Thematic Analysis of Community-Collaborative Approaches to Computing ResearchNed Cooper, Tiffanie Horne, Gillian R. Hayes, Courtney Heldreth 等CHI 2022 · 被引用 97 次
相关 Paper
- "As an Autistic Person Myself: " The Bias Paradox Around Autism in LLMsSohyeon Park, Aehong Min, Jesús Armando Beltrán Verdugo, Gillian R. HayesCHI 2025 · 被引用 13 次
- "It's the only thing I can trust": Envisioning Large Language Model Use by Autistic Workers for Communication AssistanceJiWoong Jang, Sanika Moharana, Patrick Carrington, Andrew BegelCHI 2024 · 被引用 58 次
- "The less I type, the better": How AI Language Models can Enhance or Impede Communication for AAC UsersStephanie Valencia, Richard Cave, Krystal Kallarackal, Katie Seaver 等CHI 2023 · 被引用 92 次
- Linguistic Bias in ChatGPT: Language Models Reinforce Dialect DiscriminationEve Fleisig, Genevieve Smith, Madeline Bossi, Ishita Rustagi 等EMNLP 2024 · 被引用 36 次
- AccessEval: Benchmarking Disability Bias in Large Language ModelsSrikant Panda, Amit Agarwal, Hitesh Laxmichand PatelEMNLP 2025 · 被引用 2 次
