Challenges = Data, Compute & Skills Gap
A recent report from GSMA entitled “AI for Africa: Use Cases Delivering Impact” discusses the transformative potential of artificial intelligence (AI) in addressing key development challenges across Africa, particularly in the sectors of agriculture, food security, energy, and climate action. Funded by UK Aid and supported by the GSMA and its members, the report underscores AI’s capacity to significantly contribute to achieving the Sustainable Development Goals (SDGs) on the continent.
The report analyses of over 90 AI applications across Kenya, Nigeria, and South Africa, highlighting the varied use cases and their respective impacts. These countries were chosen due to their thriving tech ecosystems and potential for leveraging AI for development.
A major focus of the report is on the role of data, particularly the challenges and opportunities associated with data annotation and data sets. In Africa, the availability of locally relevant data is limited, posing a significant barrier to the development and deployment of AI solutions tailored to the continent’s unique challenges. For instance, geospatial and remote sensing data, critical for various AI applications, are often limited and controlled by international entities, making access difficult for local developers.
The report highlights the scarcity of language data as a major issue. Many African languages lack extensive corpora, leading to biases in AI models trained predominantly on data from the Global North. This data gap limits the relevance and effectiveness of AI-enabled services for local populations. Initiatives like Mozilla Common Voice, which expands open-source language datasets, are crucial but often rely on external funding, affecting their sustainability.
Furthermore, the report discusses the infrastructure and compute capacity necessary for AI development. Africa has fewer data centers compared to other regions, which hampers local data processing capabilities and increases reliance on undersea cables that are susceptible to disruptions. The report emphasizes the need for expanding local data centers and leveraging renewable energy to support sustainable computing.
Despite these challenges, there are opportunities for growth. Investments in data centers in South Africa and Nigeria are on the rise, and countries like Kenya are beginning to build their compute infrastructure. Additionally, the report notes that mobile-first markets in Africa could benefit from edge computing, where tasks are processed on devices like smartphones, which are increasingly prevalent in the region.
The skills gap is another significant barrier to AI development in Africa. While universities offer AI-related courses, they often lag behind industry needs. There is a need for multidisciplinary skills that combine technical expertise with domain-specific knowledge to address local challenges effectively. Organizations like Data Science Nigeria are working to bridge this gap through training and mentorship programs.
To foster a thriving AI ecosystem, the report recommends several actions: investing in domain-specific and local language data, creating clear data-sharing frameworks, enhancing digital and AI skills, and building infrastructure that supports AI development. Collaborative efforts among governments, development partners, the private sector, and academia are essential to overcome these challenges and harness AI’s potential for sustainable development in Africa.
In conclusion, while AI holds immense promise for boosting Africa’s economy and addressing development challenges, significant efforts are needed to build the necessary data infrastructure, enhance compute capacity, and develop relevant skills. By addressing these gaps and fostering an inclusive and sustainable AI ecosystem, Africa can leverage AI to achieve its development goals and improve the lives of its people.
