The role of database infrastructure for the development of artificial intelligence in Africa

Artificial intelligence (AI) has been touted as the new electricity. It has also been identified as a means for organizations to cut costs, enhance their quality of services and create great economic value.

The evolution of AI applications and models in significant industries, such as medical management, e-business, manufacturing, and agribusiness, carries the potential to transform economies and livelihoods. Be that as it may, developed countries are ready to make the most gains while most of the developing countries are left behind.

Given this context, academics and researchers, companies, governments, institutions and civil society organizations with a dedicated interest in AI issues require an in-depth understanding of the differential impact of AI databases performance on various sets of users.

Accordingly, an AI database today is designed mainly with the purpose of training machine learning and deep learning models. Machine learning depends on valid data. Without quality AI databases, even the best algorithms can be rendered ineffective when they are trained on deficient, inaccurate, or irrelevant data at their learning process stage. Thus, no element is more important, for example, machine learning, than quality AI databases. The quality of this database has implications for the model’s subsequent development, as it sets a precedent for all future applications that use the same database.

Meanwhile, the database infrastructures in Africa for AI are mostly unsatisfactory because they are developed outside of Africa and so do not hold the same conventions or beliefs contextual to Africa, a problem that perpetuates biases of various kinds and has a more noticeable consequence on the use of AI applications by Africans. There is, therefore, a need to develop appropriate guidelines that will promote inclusion and non-discrimination amongst users from different cultures or parts of the world. Moreover, it must be emphasized most systems are as good as the data they make use of or that they depend on. If the data does not have integrity or objectivity, it leads to false or biased conclusions. This often can be very dangerous. Therefore, we must make sure that the data in AI databases are sound and not biased.

With the low level of digitization and infrastructure across Africa, there is a shortage of locally developed databases that are crucial for the establishment of AI systems. There is a need to design correct datasets within Africa through detailed data collection infrastructure guidelines cum standards. Expanding and strengthening diversity within the field of AI is ensuring that the experts who create and deploy these technologies come from various experiences and offer varied world views. In Africa, this diversity should include people from different parts of Africa, rather than just the more technologically developing African countries like Nigeria, South Africa, Kenya, Ghana, and Rwanda that currently overwhelm the sector.

Africa can benefit from the diversification of AI data sources because it immensely improves the quality of databases. It also offers opportunities for businesses to grab new markets and for governments to provide essential services where they are most required. It plays a vital role in building up an efficient and reliable system. So, for us to have AI systems in Africa that are fit for purpose, we must make sure that those AI systems make use of appropriately constructed databases, with the right guidelines and standards, without biases and lack of integrity, on the part of their datasets.

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