Algorithmic Gaze and the Feedback Loop of Othering: The Case for Africa

Several studies have examined the concept of algorithmic gaze in the ways that algorithms are trained to ‘see’ the ‘other’ particularly nonwestern countries and demonstrating how these views projected by algorithms usually have roots in historical biases and colonial narratives. However, few research has discussed beyond this unidirectional gaze of algorithms, particularly in the African context. In extending the historical contexts of colonialism and neo-colonialism in algorithms, This study brings to the fore how TikTok’s algorithmic behavior in content portrayal, and user engagement patterns not only reinforces stereotypes about Africa but also demonstrates that a feedback loop exists, wherein users who create content are influenced by algorithmically curated content and further contribute to this cycle of othering by creating content and interacting in ways that further inform algorithmic processes. This thus reinforces these narratives deeply rooted in colonial perspectives. These findings emphasize the importance of promoting algorithmic transparency, digital literacy, and fair representation to challenge long-standing stereotypes and promote a more accurate understanding of Africa on digital platforms. Through recommendations aimed at diversifying data, and promoting cross-cultural digital literacy, this research contributes to the broader discourse on digital representation, algorithmic fairness, and the decolonization of digital spaces.