Author: Arthur Gwagwa; Erika Kraemer-Mbula; Nagla Rizk; Isaac Rutenberg; Jeremy de Beer
Type of publication: Article
Date of publication: 2020
There is a dearth of data on all aspects of artificial intelligence (AI) in Africa, and much of the available information is thus anecdotal. Meanwhile, there is a need for African policy responses, at the national, regional, continental and international levels, aimed at ensuring that the continent’s innovators, enterprises, communities, governments, and other actors are able to reap AI’s benefits and mitigate its threats. Sound policy approaches will be needed to enable African nations to build ecosystems that are inclusive, socially beneficial, and adequately integrated with on-the-ground realities.
In contemporary African settings, both the benefits and risks of AI are readily apparent. Brandusescu et al. provide examples of innovative AI use in Kenya, Nigeria, and South Africa to address needs in health, agriculture, fintech, public transportation, and language translation. Smith and Neupane provide examples from these same three countries, as well as Uganda and Ethiopia, of beneficial AI use in point-of-care diagnostics, government service delivery, wildlife conservation, crop monitoring, water management, enterprise development, and financial services.
UN Global Pulse has published findings from its testing of AI natural language processing (NLP) tools to identify Somali social media postings with a bearing on peacebuilding and Ugandan radio content that portends social conflict. In Accra, Google’s AI Laboratory is experimenting with compressed algorithms that can run on the computing power of mobile phones. IBM’s mobile open source Hello Tractor platform is providing AI-based on-demand tractor access to Nigerian farmers.
The potential of new technologies to magnify existing inequities becomes more challenging in contexts where inequality is multi-layered. This tends to be the case in many African settings, where gender inequality is but one facet of complex and multidimensional inequalities that extend beyond income and that are rooted in various disparities-including disparities based on race, ethnicity, and social background.
AI and equity in African settings
A core equity dimension is gender, and there is evidence to suggest that African nations are experiencing a transformative “feminization” of technology entrepreneurship . Vibrant startup ecosystems that support women are emerging in Kenya, Nigeria, and South Africa, with North Africa catching up. One example is Morocco’s WaystoCap, an ambitious female-led tech startup based in Casablanca that provides a cross-border commerce platform. According to the 2017 Mastercard Index of Women’s Entrepreneurship (MIWE), Sub-Saharan Africa had the world’s highest rate of female entrepreneurs (27%), and 34.8% of businesses in Uganda, and 34.6% in Botswana, were owned by women. The study describes this level of ownership as “significantly higher than in the United States, the United Kingdom and Germany, to mention a few”. In Egypt, women are adopting AI technologies to engage in ride-sharing platform services as drivers. This is unprecedented in the country’s male-dominated taxi driving culture, and it empowers the women, not only by improving their ability to provide for their livelihoods, but also by breaking down social taboos and using digital technologies to ensure their safety.
The potential of new technologies to magnify existing inequities becomes more challenging in contexts where inequality is multi-layered. This tends to be the case in many African settings, where gender inequality is but one facet of complex and multidimensional inequalities that extend beyond income and that are rooted in various disparities-including disparities based on race, ethnicity, and social background. Such inequalities of opportunity are often aggravated by new technologies.
AI and cultural and linguistic diversity
As forcefully outlined by Kulesz, AI can be expected to have profound impacts on the diversity of widely available cultural expressions in both the developed and developing worlds and, in the absence of strong policy interventions, the impacts have the potential to be starkly negative, particularly for the world’s poor countries who are not home to the dominant AI and digital content firms.
The potential of new technologies to magnify existing inequities becomes more challenging in contexts where inequality is multi-layered. This tends to be the case in many African settings, where gender inequality is but one facet of complex and multidimensional inequalities that extend beyond income and that are rooted in various disparities-including disparities based on race, ethnicity, and social background
With respect to linguistic diversity, which is integral to cultural diversity, it is estimated that 17% of the world’s languages, many of them in Africa, are “low resource languages” in the digital realm, i.e., there are insufficient examples of use of the languages available online for the purposes of training NLP applications. These languages are marginalised by technology deployments, including AI deployments, developed in the Global North.
AI and labour in African settings
To better anticipate the impact of AI on jobs in Africa, it is important to consider the distribution of the labour force. Approximately 54% of all workers in Sub-Saharan Africa are in the agricultural sector, and in some specific countries this figure surpasses 70%. In the agricultural sector, AI has two primary uses that are, or are expected to be, of significant impact and value. First, as with other sectors, AI has significant advantages in analysing data, and it is thus useful for predicting the weather, optimising planting and harvesting schedules, determining appropriate fertiliser needs, and the like. This use of AI has the potential to increase yields and overall land productivity or efficiency, and it is unlikely to negatively affect the African labour force in the agricultural sector. Indeed, by improving the ability to predict floods and drought, optimise land usage, and increase yields, AI may increase the need for workers in the agricultural sector. This use of AI is, therefore, not necessarily competitive with human labour, and could actually be complementary to it.
At African continental and regional levels
The key African continental instrument with relevance to AI is the 2014 AU Convention on Cyber Security and Personal Data Protection. However, as of the middle of2020, only eight AU Member States had signed, ratified, and deposited the convention.
There is wide recognition on the continent that building robust African AI policy-making capacity also requires the development of a critical mass of AI skills.
At regional level, the Economic Community of West African States (ECOWAS) has adopted the 2010 Supplementary Act on Personal Data Protection within ECOWAS, which is binding on the community’s Member States. Other African regional economic bodies have also worked to produce non-binding instruments with relevance to AI-e.g., the East African Community’s (EAC’s) draft EAC Legal Framework for Cyber Laws, and the Southern African Development Community’s (SADC’s) Model Law on Data Protection in 2012.
There is wide recognition on the continent that building robust African AI policy-making capacity also requires the development of a critical mass of AI skills. Accordingly, the AI4D Africa initiative has pledged to support not only the aforementioned policy research bodies, but also African AI networks, labs, and scholarships.
There is wide recognition on the continent that building robust African AI policy-making capacity also requires the development of a critical mass of AI skills
From certain perspectives, it can be argued that there is a high level of diversity of AI deployment on the African continent. As revealed in this article, one aspect of diversity is in the types of problems that are being addressed by AI. From financial inclusion to combatting cultural and linguistic marginalisation, AI innovations are aimed at many different aspects of African society, economy, and government. Another form of diversity is in the people implementing AI solutions, and in this regard, the relatively high level of participation by women in African entrepreneurship is encouraging.
Diversity of location is also noteworthy-while AI is clearly developing in countries that are well known as technology hubs (e.g., Kenya, Nigeria, and South Africa), there are also significant AI-focused activities in countries that are less frequently recognised for cutting-edge digital adoption (e.g., Uganda and Ethiopia). In contrast, government policy is an area where there is less diversity, as the vast majority of African countries lack a dedicated AI policy instrument.
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