Whose Voice Speaks? Generative AI, Algorithmic Justice, and Strategic Communication in African Media

Abstract

Generative artificial intelligence (AI) is reshaping strategic communication across Africa, yet the tools are trained overwhelmingly on Global-North data and carry its representational defaults. This paper asks whose voice such systems amplify, and whose they silence, when African organisations use them to speak to their own publics. Using qualitative documentary analysis of publicly reported cases in which African organisations used generative AI in outward-facing communication and drew criticism on representational or authenticity grounds, and applying reflexive thematic analysis through a decolonial, algorithmic-justice lens, the study constructs three themes. First, a double bind of representation: uncritical AI fails along two opposite axes, erasing the local subject by rendering it as Global-North, or essentialising it by reducing it to stereotyped African signifiers, both rooted in training-data geography. Second, a political economy of authenticity, in which the turn to AI displaces local creative labour and audiences read the resulting communication as inauthentic partly because they infer that displacement. Third, the public as algorithmic watchdog, whereby affected publics, not regulators, detect and contest AI use, performing a grassroots decolonial critique. A contrast case of a brand publicly refusing AI underscores that authenticity has become a contested market value. The paper offers a conceptual model of algorithmic representational justice and argues that just AI use in African strategic communication requires re-centring the displaced local voice, not merely improving model outputs. Cases cluster in Kenya and South Africa, taken here as an information-rich entry point into a wider Global-South dynamic.