mlsanityData to 7 Oct 2026

Think Outside the Data: Colonial Biases and Systemic Issues in Automated Moderation Pipelines for Low-Resource Languages

arXiv 2501.13836 ↗First posted 23 Jan 2025cs.CL cs.HCcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 14 of its 92 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

14 of 92 references.

References · 14

  1. 2021Quality at a Glance: An Audit of Web-Crawled Multilingual Datasetscited by 241
  2. 2020Decolonial AI: Decolonial Theory as Sociotechnical Foresight in Artificial Intelligencecited by 100
  3. 2019Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?cited by 64
  4. 2023Factoring the Matrix of Domination: A Critical Review and Reimagination of Intersectionality in AI Fairnesscited by 26
  5. 2024Opportunities and Challenges of Large Language Models for Low-Resource Languages in Humanities Researchcited by 26
  6. 2023A Material Lens on Coloniality in NLPcited by 19
  7. 2024The "Colonial Impulse" of Natural Language Processing: An Audit of Bengali Sentiment Analysis Tools and Their Identity-based Biasescited by 16
  8. 2022HateCheckHIn: Evaluating Hindi Hate Speech Detection Modelscited by 15
  9. 2019Writing Across the World's Languages: Deep Internationalization for Gboard, the Google Keyboardcited by 10
  10. 2023Decolonial AI Alignment: Openness, Vi'sedsa-Dharma, and Including Excluded Knowledgescited by 10
  11. 2024Vernacularizing Taxonomies of Harm is Essential for Operationalizing Holistic AI Safetycited by 4
  12. 2023Operationalizing content moderation "accuracy" in the Digital Services Actcited by 2
2 more
  1. 2021The Coloniality of Data Work in Latin Americacited by 1
  2. 2024WhatsApp Explorer: A Data Donation Tool To Facilitate Research on WhatsAppcited by 1

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  5. 2024DOSA: A Dataset of Social Artifacts from Different Indian Geographical Subcultures
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  7. 2026An Empirical Study of Collective Behaviors and Social Dynamics in Large Language Model Agents
  8. 2022No Language Left Behind: Scaling Human-Centered Machine Translation