Algorithmic power and African indigenous languages: search engine autocomplete and the global multilingual Internet
Peter Chonka, Stephanie Diepeveen, Yidnekachew Haile
Abstract
Predictive language technologies – such as Google Search’s Autocomplete – constitute forms of algorithmic power that reflect and compound global power imbalances between Western technology companies and multilingual Internet users in the global South. Increasing attention is being paid to predictive language technologies and their impacts on individual users and public discourse. However, there is a lack of scholarship on how such technologies interact with African languages. Addressing this gap, the article presents data from experimentation with autocomplete predictions/suggestions for gendered or politicised keywords in Amharic, Kiswahili and Somali. It demonstrates that autocomplete functions for these languages and how users may be exposed to harmful content due to an apparent lack of filtering of problematic ‘predictions’. Drawing on debates on algorithmic power and digital colonialism, the article demonstrates that global power imbalances manifest here not through a lack of online African indigenous language content, but rather in regard to the moderation of content across diverse cultural and linguistic contexts. This raises dilemmas for actors invested in the multilingual Internet between risks of digital surveillance and effective platform oversight, which could prevent algorithmic harms to users engaging with platforms in a myriad of languages and diverse socio-cultural and political environments.