低资源语言内容审核困局源于殖民历史与系统性不公，非单纯数据不足。
Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languages
- 通过22位专家访谈，揭示技术设计与政治经济结构的双重制约
- 英语中心模型忽略多语混杂、形态复杂的低资源语言特征
- 呼吁多方协作提升本地研究能力，打破数据垄断
全球南方地区多数社交媒体用户使用本土语言，但有害内容检测的AI系统在低资源语言上表现不佳。本文通过对四位低资源语言（泰米尔语、斯瓦希里语、马格里布阿拉伯语、克丘亚语）中22位AI专家的半结构化访谈，揭示自动化内容审核系统的深层问题。除数据稀缺外，科技公司对用户数据的垄断及对低利润市场的忽视加剧了历史不平等。即便数据充足，现有以英语为中心、依赖大量数据的语言模型和预处理方法，也未能适配形态复杂、语言多样且存在代码混用的低资源语言。这些问题不仅是技术短板，更根植于殖民时期对非西方语言的压制。文章主张通过多利益相关方合作，增强本地研究能力，推动数据民主化，发展语言感知的解决方案，以改善低资源语言的内容审核。
原文摘要 · Abstract (English)
Most social media users come from the Global South, where harmful content usually appears in local languages. Yet, AI-driven moderation systems struggle with low-resource languages spoken in these regions. Through semi-structured interviews with 22 AI experts working on harmful content detection in four low-resource languages: Tamil (South Asia), Swahili (East Africa), Maghrebi Arabic (North Africa), and Quechua (South America)--we examine systemic issues in building automated moderation tools for these languages. Our findings reveal that beyond data scarcity, socio-political factors such as tech companies' monopoly on user data and lack of investment in moderation for low-profit Global South markets exacerbate historic inequities. Even if more data were available, the English-centric and data-intensive design of language models and preprocessing techniques overlooks the need to design for morphologically complex, linguistically diverse, and code-mixed languages. We argue these limitations are not just technical gaps caused by "data scarcity" but reflect structural inequities, rooted in colonial suppression of non-Western languages. We discuss multi-stakeholder approaches to strengthen local research capacity, democratize data access, and support language-aware solutions to improve automated moderation for low-resource languages.
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