The AI Digital Divide – An African Perspective

This blog post is part of a series by Chioma Nwaodike, a lawyer from Nigeria who is currently an Atlas Corps Internet Policy Fellow at Internews. Her blog posts discuss a broad range of topics around digital rights and internet freedom.

For this month’s blog post, I want to explore the broad and ever-evolving world of Artificial Intelligence (AI). For many of us, AI conjures up images from science fiction of advanced, human-like robots programmed to do household chores and operate alongside humans. However, these popular conceptions fail to capture fully the variety of ways in which AI is currently impacting people around the world. In this post, I will examine what AI looks like today, some of the current debates around the social and economic implications of AI, and the future of AI on my continent, Africa. It is essential that we engage in discussion about artificial intelligence from a developing country perspective in order to better understand how emerging technologies impact regions and countries differentially depending on social, economic, and political conditions.

The Basics: What is AI and where is it used?

Machine learning forms the basis of all things AI. While many people primarily associate AI with robotics, AI in its most basic form uses algorithms to make predictions. Machine learning involves applying computer science, calculation, and mathematics to large datasets in order to deduce patterns and learn from and adapt to these patterns. Machine learning can allow individuals and organizations to gain insights that would otherwise be impossible for humans to observe and analyze. Thus, AI can enable better decision making and can assist in tackling complex challenges. In short, AI relies on smart machines that process data in order to mimic and predict human behavior.

Far from science fiction, AI is deployed in almost all the technologies we use daily. Apple devices, Google’s facial recognition system, Instagram face filters, Facebook news feed, Netflix, and Spotify all draw on machine learning to process and analyze large amounts of data in order to improve and personalize products and services. Governments and public entities also use AI for a wide variety of applications. For example, facial recognition is used by police to track and identify criminals or by customs and border control when scanning passports and cities, and municipalities develop customer service chatbots to help with public service delivery.

Artificial Intelligence is beneficial for many aspects of our lives. For instance, in the health sector, it can be used to identify and track infectious diseases, to assist those with physical disabilities, to provide novel forms of information on agricultural solutions for local communities, and within voice recognition for visual or hearing impairments. However, as with all emerging technologies, it is important to consider the societal and policy implications of AI as well as the ways in which diverse populations can be variably impacted by the deployment of these new technologies. This is especially the case for those who live in developing countries, where the debate about the productive benefits and societal impact of AI needs to be contextualized within the social and economic realities. In the following section, I will explore the questions and concerns about current and future uses of machine learning and the impacts on individuals’ human rights in African countries.

The AI Digital Divide in Africa


As AI relies on large amounts of data, the first set of policy questions and concerns relates to access and cost of processing data, as well as how African citizens’ data will be managed to protect privacy and rights.

Only 28.2% of people in Africa have access to the internet. As data is the prerequisite asset allowing AI systems to function, questions about connectivity and unequal access to data also must be considered. For instance, in Nigeria data is expensive and internet connectivity variable. These disadvantages developers and AI entrepreneurs. Without reliable core infrastructure, affordable data plans, and easy access to technologies, current digital divides will only be exacerbated with the rise and continued advancement of AI. This is especially the case in areas in which access to the internet is limited to low-bandwidth mobile, as many AI applications require faster internet speeds and better software.

With slower and less reliable internet access, African developers operate at a competitive disadvantage. Tech companies based in Europe and the US are thus able to draw materials or data from the developing world, and then “import” them back as finished goods (software). In this way, AI risks becoming an extractive industry that pulls resources out of the country to the benefit of others, designing closed source software that often cannot be used by African developers.


In addition to understanding the inequalities inherent in how artificial intelligence and software is developed by and for Africans, we need to better understand how data, especially sensitive data such as biometrics (voice, facial, fingerprints, etc.) is acquired, shared, and used. As I explored in my previous blog post, there is far too little transparency around data collection and few legal mechanisms for data protection within developing countries. What are the safety measures in place to guard against a data leak or hack? How can we ensure that data is not exploited or misused by companies or developers? To gain trust and confidence in AI systems, individuals need better ways to control and secure their data online, and entities engaged in data collection need to better disclose the ways in which data is collected, shared, sold, stored, and applied.


There are different gaps that exist both across and within the different countries in Africa, between urban and rural areas, and according to gender and language. The connectivity challenge in the region leads to AI produced for non-African languages, with less local content and African-produced open-source AI technology, and a dearth of resources for local training.

To understand these challenges, let me provide an example. If I ask SIRI in my native language about the weather in Nigeria, it is certain that I’ll not receive a correct answer or that SIRI will even understand this simple question. Applications that employ AI are largely built-in major languages such as English and Chinese, and as of now, I have not seen any such application that understands many African languages. If these barriers are not eliminated, with more emphasis placed on AI development for local languages and local content, AI will aggravate the digital divide and exclude individuals from the benefits of artificial intelligence.

In order to build more inclusive AI technologies for diverse groups of Africans, it is necessary to tie initiatives to local communities and their diverse needs. For instance, to address facial recognition systems misidentifying black faces, AI technologies should be designed by Africans for local issues. Locals understand the unique needs of each community. In contexts where digital literacy is a barrier, local users should be consulted throughout the development of AI applications. This will create an inclusive environment where, for example, the illiterate farmer in a rural area can better access information on agricultural innovations and pricing using voice-based AI applications, while a local teacher can find new curricula for her students.


Africa has the highest youth population in the world, with 60% of the African population under 25 years. How do developments in AI differentially impact this younger generation? To strive in this world of AI and to be fully empowered in digital spaces, specific skills are required. Digital literacy remains a challenge, with few avenues for formal training around not only the use of digital technologies but also more advanced technical skills.

Data collected in developing countries, compiled by the International Telecommunications Union (ITU), reveals that major barriers to internet uptake in developing countries is the lack of education and skills. With the dawn of the 4th industrial revolution, it is important to prepare the African youth with digital skills at all levels to handle smart machines.

Policy Framework

Policies enabling African AI development must be rooted in a human rights perspective and respect international norms in addressing the challenges highlighted above. Any meaningful dialogue around policy requires input and collaboration with a wide variety of stakeholders including international bodies such as the ITU and UN, regional African organizations, technology companies from inside and outside Africa, and civil society groups that focus on protecting African citizens and marginalized populations online. Measures such as fairness, accountability, transparency as well as safety and security should be at the core of any technical, ethical, and regulatory framework.

Data Politics and Access: First and foremost, data protection and privacy must be central to the debate around artificial intelligence in African countries. The African Union, in agreement with State members, the United Nations, and other international bodies should unlock the power of data by developing a standard data governance framework that does not discriminate based on region of the world and takes into account existing global inequities in technology production and consumption. The collaboration of these entities will significantly impact the implementation of a policy structure.
This framework can enable governments, tech companies, NGOs, and entities across the public and private sectors to share safe and secure data while protecting the right to privacy. While this framework can be shared regionally and globally for reference when making policies, it is necessary that local actors are able to contextualize AI policy approaches within specific country circumstances and with local stakeholder perspectives.

National policies should include principles relating to the production of both proprietary and open-source software, management of data, and affordable access. An appropriate regulatory framework linking data access and protection can create an enabling environment for the development of artificial intelligence in Africa.  Public and private stakeholders should work together to develop common resources, databases, platforms, and tools that are open and encourage African technology development.

Connectivity: Connectivity remains a challenge on the continent, impacting the ability of consumers to access AI-enabled applications and services as well as the ability of African developers to build innovative new technologies by and for Africans. To promote more equitable practices and prevent exploitation, there must be renewed efforts to invest in core infrastructure in order to ensure affordable broadband and adequate power.

Local content and language: To increase investment in local content and African languages, national policies must include principles that encourage consultation of local users when developing AI applications. Drawing on rural churches, schools, and community radio stations when developing local content will create an inclusive environment where content is rooted in local communities and culture.

Education: The future of AI-oriented work in developing countries necessitates the inclusion of technology into school curricula as well as the development of linkages with higher education programs. It is necessary for students and teachers to build AI-oriented curricula that integrate technology and technological concepts into a variety of contextual learning (science, art, business, etc.). This will enable youth to acquire essential ICT skills in order to promote responsible technology consumption and build African AI industries.

Connecting high-level education programs on AI to core infrastructure tied to community hubs can also offer opportunities for younger Africans to lead the way in African-produced AI applications. Proper education and knowledge of AI systems will give this next generation the skills required to be productive through the “4th industrial revolution” and reduce the high rate of unemployment in the region.

Finally, it is essential to develop policies and provide resources for user-friendly digital literacy programs and IT skill training for marginalized groups, so that everyone is able to fully access services, programs, and platforms, and make smart decisions online. These education programs should take into consideration the specific skills needed by audiences such as women and those from rural areas. Programs should offer lower-level learning around digital literacy and safety skills, as well as opportunities for more advanced and longer-term learning on data management, coding, and tech entrepreneurship.