Bias: The Hidden Tariff of AI for Industry in Africa

Regan Shen

Keywords: Bias, AI, Africa

TL;DR: Dealing with Bias in AI models, when trying to utilise these models in industry in Africa

Abstract: The growing adoption of Artificial Intelligence (AI) across industries has been accelerated by the availability of pre-trained models and open-source tools. These models offer practical benefits, enabling organizations to integrate AI solutions without the need for costly and time-intensive development from scratch. However, this convenience often comes at the cost of inherited bias—especially when models are applied in contexts that differ from those they were originally trained for. This paper explores the challenges that arise when AI models, developed with limited demographic diversity, are deployed in African industry settings. Through a real-world case study, we examine how these biases manifest in practice, the limitations of common mitigation strategies, and the systemic under representation of African datasets. We highlight the need for more representative datasets, local research capacity, and structural investment to ensure fair and effective AI adoption on the continent.

Submission Number: 6