Application of Artificial Intelligence in the Management of Climate Change-Related Health Issues in Africa

Artificial Intelligence (AI) has the potential to be transformative for development, but Africa is currently facing a fragmented and challenging "AI divide". This paper provides an empirical analysis of the current state of the AI landscape and how it compares with Africa's technological preparedness for the future. In our analysis, we approach the "AI Divide" from three angles: infrastructure, accessibility, and human capacity. First, we look at the physical constraints that prevent Africa from integrating digitally. We then evaluate the human-centred factors that limit the development of AI technology on the continent. Finally, we examine the human capacity to develop AI systems on the continent and provide three focused case studies. Our investigation shows that the physical infrastructure needed to build an AI economy on the continent is lagging, with only 38% internet penetration, poor broadband coverage and less than 1% of all data centres globally. Other constraints include high data costs relative to income, gender-based digital divides, and the need to build more representative NLP models that can understand Africa's native languages. However, there are positive trends towards the emergence of local initiatives and grassroots movements, such as startups and universities, contributing to AI development on the continent. Based on these findings, we provide concrete recommendations to policymakers to help develop a more comprehensive and equitable AI ecosystem on the African continent.

AI development's current trajectory risks automating and amplifying the North-South divide in the global climate information system. Frontier models are built almost exclusively in the Global North, and this inequality continues through inputs, processes, and outputs, from biased training data to unrepresentative validation, disproportionately affecting vulnerable regions. Addressing these disparities requires a Climate Digital Public Infrastructure, evaluation metrics centring well-being, and knowledge co-production to foster resilience rather than inequity.

Large Language Models (LLMs) depend fundamentally on the diversity, depth, and factual rigor of their pre-training corpora. However, contemporary foundational pre-training datasets (such as The Pile, S2ORC, and Dolma) exhibit a severe structural omission: African scientific, medical, legal, and agricultural scholarship constitutes less than 0.5% of global pre-training tokens. Consequently, state-of-the-art models suffer from systematic failure modes when reasoning about tropical epidemiology, localized smallholder agro-ecologies, regional economic governance, and indigenous linguistic structures. To resolve this representation deficit and advance African intellectual sovereignty in artificial intelligence, we introduce Africa-Papers, the largest curated multi-domain Pan-African academic pre-training corpus to date. Comprising over 844,807 full-text papers spanning 10.15 billion tokens across 879 Parquet shards, Africa-Papers unifies four primary scientific streams: (1) continental peer-reviewed journals indexed in DOAJ across 31 African nations, (2) the CGIAR CGSpace agricultural repository, (3) African-authored and African-focused STEM/AI preprints on arXiv, and (4) open-access international publications by scholars affiliated with African institutions indexed via OpenAlex. The entire corpus is processed into layout-aware, clean Markdown preserving section hierarchies, mathematical equations, and tabular data, accompanied by exact BPE token counts and a standardized 8-domain taxonomy. We release Africa-Papers openly on the Hugging Face Hub under the AfriSpeech organization to accelerate continual pre-training and domain evaluation for foundation models globally.