以残疾人为先的AI数据集标注：与口吃者共同设计 stuttered speech 注标指南
Disability-First AI Dataset Annotation: Co-designing Stuttered Speech Annotation Guidelines with People Who Stutter
AI总结：
本文通过与口吃者共同设计标注指南，探讨了如何通过整合口吃者的 lived experiences 提高数据集质量，并揭示了残疾经验复杂性与静态标签之间的张力。
摘要：
尽管努力增加残疾人在AI数据集中的代表性，但可及性数据集通常由没有残疾特定专业知识的众包工人进行标注，导致标签不一致或不准确。本文通过一个对口吃者（PWS）语音数据进行标注的案例研究，探讨这些标注挑战。鉴于口吃的变化性和对口吃表现的不同看法，标注和转录口吃语音仍然困难，即使对于受过训练的专业人员也是如此。通过与PWS和领域专家的访谈和共同设计研讨会，我们识别了口吃语音标注中的挑战，并开发了将PWS的 lived experiences 融入标注过程的实践。我们的发现突显了身体知识在提高数据集质量方面的价值，同时揭示了残疾经验的复杂性与静态标签的僵化之间的张力。我们最后得出关于以残疾人为先和多重性意识的数据解释方法在AI管道中的启示。
英文摘要：
Despite efforts to increase the representation of disabled people in AI datasets, accessibility datasets are often annotated by crowdworkers without disability-specific expertise, leading to inconsistent or inaccurate labels. This paper examines these annotation challenges through a case study of annotating speech data from people who stutter (PWS). Given the variability of stuttering and differing views on how it manifests, annotating and transcribing stuttered speech remains difficult, even for trained professionals. Through interviews and co-design workshops with PWS and domain experts, we identify challenges in stuttered speech annotation and develop practices that integrate the lived experiences of PWS into the annotation process. Our findings highlight the value of embodied knowledge in improving dataset quality, while revealing tensions between the complexity of disability experiences and the rigidity of static labels. We conclude with implications for disability-first and multiplicity-aware approaches to data interpretation across the AI pipeline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
