Information gathering is not an easy task in Africa. This fundamental observation is hardly debatable: the collection, processing, storage, and exploitation of data are not virtues widely shared across the continent.
Yet AI thrives on data—without it, it holds little value.
The question of AI’s contribution to information retrieval in Africa requires first addressing the structural shortcomings that hinder the establishment of reliable databases. Nevertheless, the stakes and potential benefits of AI in this domain are considerable.
- Acknowledging the Weakness of Digital Infrastructure
This assessment arises from an analysis of several factors, including but not limited to:
- Limited internet access. In 2023, less than 40% of Africa’s population had access to the Internet, with significant disparities between urban and rural areas.
• Poor connection quality. Internet connectivity remains a major issue in Africa. In addition to the thorny problem of exorbitant costs, there are issues of low bandwidth, high latency, and the all-too-frequent power outages.
• The lack of local data centres compels Africa to rely on foreign servers, thereby slowing digital services.
• Low mobile coverage also deserves mention. While mobile networks are more widespread than fixed-line ones, 4G—and even more so, 5G—remain scarcely deployed.
- Promising Prospects
Artificial intelligence can increasingly impact information retrieval in Africa, with notable benefits across several key sectors.
This article does not delve into the major role AI already plays in other regions of the world in terms of enhancing research, fostering creativity, or generating wealth. Instead, it focuses on the specific contributions related to data and information that AI is eager to optimise in Africa.
In terms of improving access to information, notable examples include:
• African language processing – AI can play a major role in developing natural language processing tools for local languages (such as Swahili, Wolof, Hausa, and Féé-féé), thereby facilitating information retrieval in languages historically underrepresented online.
• Automatic translation – through systems that make global content accessible in local languages and vice versa.
Given the poor quality of internet connectivity in Africa, AI can enable offline or low-bandwidth access via lightweight systems (edge computing) that allow for information searches even with limited connectivity.
AI’s contribution to the collection and structuring of local data is also notable. AI enables intelligent aggregation of information by supporting the collection, filtering, and organisation of data from disparate sources.
Moreover, AI facilitates information extraction through text mining systems that automatically extract facts, trends, or statistics from unstructured texts.
In the field of academic research support, the value of AI is well established.
• For automated monitoring, researchers can use AI tools to automatically receive the latest relevant publications in their field.
• In bibliometric analysis, AI can help map African scientific research, identify key researchers, collaborations, or thematic gaps.
This brief paper concludes with some concrete areas of application of AI in information retrieval in Africa:
• In agriculture, AI can help farmers access personalised information about crops, diseases, or weather forecasts.
• In education, AI-powered platforms can adapt content to meet the needs of pupils or students, even in rural areas.
AI’s contribution to information retrieval in Africa still has a bright future ahead!
Jean-Marie TENGANG
