With AI policy updates emerging from the U.S. and Europe regularly, it’s essential to monitor the global landscape. This week, the focus shifts to The African Union’s digital transformation session, convening leaders from 55 African nations for extensive discussions on the continent’s digital future and AI strategy.
Hosted in Ethiopia, the summit commenced with three days of expert gatherings, emphasizing the Draft Conceptual Framework of the Continental Strategy on Artificial Intelligence. This framework, initially outlined in August and still evolving, aims to shape an ethical and economically prosperous AI strategy for Africa. It targets vital sectors such as education, healthcare, agriculture, and finance. The session facilitated ongoing discussions on the framework, delving into defining principles, strategic objectives, and considerations regarding security and responsible AI deployment.
Members underscored AI’s pivotal role in achieving Africa’s Sustainable Development Goals (SDGs) and Agenda 2063, a 50-year development roadmap dubbed Africa’s “blueprint” for attaining economic growth, political autonomy, democracy, gender equality, and cultural preservation.
“Africa recognizes AI’s significance due to its far-reaching economic, social, and political impact. AI technologies can spur economic progress, foster innovation, and create job opportunities. Furthermore, they can bolster education and safeguard African languages,” as stated in a press release from the African Union regarding the session.
The discussions culminated in member states committing to advancing digitalization efforts encompassing climate action, infrastructure development, and energy initiatives. The draft declaration emphasized the urgency of data governance amid AI proliferation, urging the African Union to aid member states in establishing robust national data governance frameworks.
Despite the burgeoning AI landscape in Africa, dominated by startups, organizations, and conferences, global tech giants have predominantly led the narrative and reaped the rewards. The prevalence of Western-centric concepts in AI training data has skewed outputs, limiting applicability and potentially causing harm to diverse populations. Moreover, pivotal decisions on global AI policy are being made without substantial African representation.
Africa has witnessed a significant brain drain in AI talent as proficient individuals are lured abroad by well-resourced tech companies. While countries like Kenya and Uganda contribute significantly to foundational AI models, the workforce, particularly data labelers, often endure low wages without reaping the technology’s benefits.
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