Mitigating Biases in Training Data: Technical and Legal Challenges for Sub-Saharan Africa

Oluka, Alexander

Mitigating Biases in Training Data: Technical and Legal Challenges for Sub-Saharan Africa

Authors

Alexander Oluka

Abstract

The study examines the challenges of mitigating biases in AI training data within Sub-Saharan Africa. A qualitative research approach with semi-structured interviews was employed to gather insights from eight participants with law, IT, and academic background. Thematic analysis was utilised to categorise the data into key themes, revealing insights into the complexities of developing fair AI technologies that reflect the socio-cultural diversity of the region. The findings emphasise the importance of incorporating local values and ethical considerations into AI development and highlight the need for enhanced collaborative efforts to establish resilient, culturally sensitive AI governance frame-works. The research contributes to the broader discourse on ethical AI deployment in diverse global contexts.

Suggested Citation (APA 7th)

Oluka, A. (2024). Mitigating Biases in Training Data: Technical and Legal Challenges for Sub-Saharan Africa. International Journal of Applied Research in Business and Management, 5(1), 209-224. https://doi.org/10.51137/ijarbm.2024.5.1.10