African languages are under-represented in artificial intelligence (AI) systems due to limited language data, excluding millions from digital participation in their native languages. Factors such as multilingual complexity, foreign-language-dominant policies, weak institutional backing and lack of digital infrastructure contribute to the low-resource classification of African languages. “Language data flaring” — paralleling gas flaring — captures the systemic neglect and poor management of African language data leading to data undercollection, poor storage and limited use in AI. Addressing the gap requires policies that integrate African languages into national digital agendas, support documentation, fund projects and foster inclusive, collaborative AI development. Community-led documentation, open-source tools and growing recognition of linguistic diversity in AI offer promising paths forward.
