Decolonizing Algorithms: AI-Driven Media and the Reclamation of African Linguistic Identities

Abstract

This study examines the influence of artificial intelligence (AI)-driven media platforms on South Africa's indigenous languages—Setswana, Tshivenda, and Xitsonga—in the country's diverse linguistic environment. It examines how algorithmic biases, often rooted in colonial linguistic hierarchies, diminish the visibility and vitality of these languages in digital media. Using a mixed-methods approach, the study combines algorithmic audits of AI platforms (e.g., social media and natural language processing tools) to evaluate content visibility and translation accuracy, interviews with AI developers and media practitioners to assess linguistic diversity in design, and focus groups with rural communities to gather user experiences. Results indicate that limited training data and a focus on dominant languages, such as English, marginalize these indigenous languages, with audits revealing error rates of 30% to 42% in translation and voice recognition for these languages. Nonetheless, community-driven innovations demonstrate potential for creating inclusive AI solutions. The study proposes a decolonial framework for designing AI technologies that prioritize African linguistic rights and epistemologies, contributing to a nuanced understanding of AI's role in Africa's media landscape. This study is among the first to integrate algorithmic audits with community ethnography to reveal how AI systems shape Africa's linguistic diversity and to propose decolonial design principles for inclusive AI futures.