迈向针对非裔美国人英语使用者的负责任自动语音识别：语音技术中偏见与公平性的范围综述
Toward Responsible ASR for African American English Speakers: A Scoping Review of Bias and Equity in Speech Technology
AI总结：
本研究通过范围综述分析面向非裔美国人英语使用者的ASR及相关技术中公平性与偏见的研究现状，提出以治理为中心的ASR生命周期框架，为解决语音AI语言边缘化提供跨学科方向。
摘要：
本范围文献综述探讨了在面向非裔美国人英语（AAE）使用者及其他语言多样化群体的自动语音识别（ASR）和相邻语音语言技术（SLT）中，公平性、偏见与公平性如何被概念化和操作化。基于人机交互（HCI）、机器学习/自然语言处理（ML/NLP）及社会语言学领域的44篇同行评审文献，我们确定了四大研究领域：（1）研究者如何理解ASR相关危害；（2）涵盖收集、整理、标注及模型训练的包容性数据实践；（3）语言包容性的方法与理论途径；（4）更公平系统的新兴实践与设计建议。尽管技术公平干预不断增多，但本综述强调了以治理为中心的方法存在关键缺口，此类方法强调社区能动性、语言正义及参与式问责。我们提出以治理为中心的ASR生命周期作为负责任ASR开发的新兴跨学科框架，并为寻求解决语音AI系统中语言边缘化问题的研究者、从业者及政策制定者提供启示。
英文摘要：
This scoping literature review examines how fairness, bias, and equity are conceptualized and operationalized in Automatic Speech Recognition (ASR) and adjacent speech and language technologies (SLT) for African American English (AAE) speakers and other linguistically diverse communities. Drawing from 44 peer-reviewed publications across Human-Computer Interaction (HCI), Machine Learning/Natural Language Processing (ML/NLP), and Sociolinguistics, we identify four major areas of inquiry: (1) how researchers understand ASR-related harms; (2) inclusive data practices spanning collection, curation, annotation, and model training; (3) methodological and theoretical approaches to linguistic inclusion; and (4) emerging practices and design recommendations for more equitable systems. While technical fairness interventions are growing, our review highlights a critical gap in governance-centered approaches that foreground community agency, linguistic justice, and participatory accountability. We propose a governance-centered ASR lifecycle as an emergent interdisciplinary framework for responsible ASR development and offer implications for researchers, practitioners, and policymakers seeking to address language marginalization in speech AI systems.
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。
