Developing and Deploying End-to-End Machine Learning Systems for Social Impact: A Rubric and Practical Artificial Intelligence Case Studies From African Contexts

Abstract

Artificial intelligence (AI) and machine learning have demonstrated the potential to provide solutions to societal challenges, for example, automated crop diagnostics for smallholder farmers, environmental pollution modelling and prediction for cities and machine translation systems for languages that enable information access and communication for segments of the population who are unable to speak or write official languages, among others. Despite the potential of AI, the practical and technical issues related to its development and deployment in the African context are the least documented and understood. The development and deployment of AI for social impact systems in the developing world present new intricacies and requirements emanating from the unique technology and social ecosystems in these settings. This paper provides a rubric for developing and deploying AI systems for social impact with a focus on the African context. The rubric is derived from the analysis of a series of selected real-world case studies of AI applications in Africa. We assessed the selected AI case studies against the proposed rubric. The rubric and examples of AI applications presented in this paper are expected to contribute to the development and application of AI systems in other African contexts.