Data colonialism and indigenous languages in AI: a critical review of existing initiatives and their struggles with data sovereignty

Abstract This article critically reviews recent initiatives to employ artificial intelligence (AI), particularly large language models (LLMs), for the revitalization of Indigenous languages. Structured by geographical contexts, the analysis includes Irish Gaelic (Europe), Māori (Aotearoa/New Zealand, Oceania), Guaraní (Paraguay/Bolivia, South America), and Inuktitut (Canada, North America). Applying a theoretical framework grounded in data colonialism and Indigenous data sovereignty, the article examines the key achievements in different regional endeavors, as well as investigate how government-led projects and Big Tech collaborations across these diverse contexts navigate (or fail to navigate) issues of data extraction, community consent, cultural representation, and ownership. Through this lens, the article identifies specific ethical pitfalls as well as commendable practices that either reproduce colonial dynamics or empower Indigenous communities. This critique emphasizes regional and contextual nuances, arguing that authentic community agency and rigorous adherence to Indigenous data sovereignty principles are vital to ensuring ethical AI practices and meaningful linguistic revitalization.