Study: AI Success in Africa Depends on Availability of Local Language Data and AI Talent

Scarcity of Local Language Data Limits AI Relevance in Africa

While AI is predicted to boost Africa’s economy and support the Sustainable Development Goals (SDGs) on the continent, a recent study found that the technology’s success hinges on the availability of locally relevant data. According to the GSMA report, the scarcity of local language data limits the relevance of AI-enabled services, which in turn constrains the technology’s potential impact.

The findings of the GSMA study, a U.K. Aid-funded initiative, are particularly timely. African countries and bodies like the African Union (AU) have recently laid out their AI strategies. As Bitcoin.com News reported, the AU adopted an AI strategy to drive the continent’s digital development.

However, despite being framed as a cure-all for many of Africa’s problems, data limitations and other barriers significantly diminish AI’s potential on the continent. To illustrate, the report identifies the high cost of graphic processing units (GPUs) and cloud computing as hurdles to AI deployment and adoption. Additionally, the GSMA study highlights how Africa’s AI skill gap influences the pace of adoption.

“Across countries, a significant skills gap still undermines the development of the AI ecosystem and use cases. While universities offer AI-related courses, they often fail to keep pace with industry needs, and students have limited opportunities for practical learning and hands-on experiences. There is also a disproportionate focus on core AI skills, such as ML and data science, with less emphasis on building the multidisciplinary skillsets needed to leverage AI to address pressing socioeconomic challenges,” the study report stated.

The report also notes that even in African countries with solid digital foundations, such as Kenya, Nigeria, and South Africa, key challenges persist within the ecosystem. Moreover, countries with AI frameworks are still in their infancy, highlighting the need to shift from policy formulation to implementation and ensure the ethical, responsible, and safe use of AI.

Meanwhile, the GSMA report said African countries should prioritize investing in domain-specific and local language data, as well as adopting participatory approaches to data collection to overcome barriers hindering AI’s development. Furthermore, the report calls on countries to foster academic-industry collaboration, raise awareness, and build capacity in the public sector. These steps will ensure a constant supply of AI and informed policymaking.

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