Algorithmic Colonisation of Africa Read

Abeba Birhane at The Elephant: “The African equivalents of Silicon Valley’s tech start-ups can be found in every possible sphere of life around all corners of the continent—in “Sheba Valley” in Addis Abeba, “Yabacon Valley” in Lagos, and “Silicon Savannah” in Nairobi, to name a few—all pursuing “cutting-edge innovations” in sectors like banking, finance, healthcare, and education. They are headed by technologists and those in finance from both within and outside the continent who seemingly want to “solve” society’s problems, using data and AI to provide quick “solutions”. As a result, the attempt to “solve” social problems with technology is exactly where problems arise. Complex cultural, moral, and political problems that are inherently embedded in history and context are reduced to problems that can be measured and quantified—matters that can be “fixed” with the latest algorithm.

As dynamic and interactive human activities and processes are automated, they are inherently simplified to the engineers’ and tech corporations’ subjective notions of what they mean. The reduction of complex social problems to a matter that can be “solved” by technology also treats people as passive objects for manipulation. Humans, however, far from being passive objects, are active meaning-seekers embedded in dynamic social, cultural, and historical backgrounds.

The discourse around “data mining”, “abundance of data”, and “data-rich continent” shows the extent to which the individual behind each data point is disregarded. This muting of the individual—a person with fears, emotions, dreams, and hopes—is symptomatic of how little attention is given to matters such as people’s well-being and consent, which should be the primary concerns if the goal is indeed to “help” those in need. Furthermore, this discourse of “mining” people for data is reminiscent of the coloniser’s attitude that declares humans as raw material free for the taking. Complex cultural, moral, and political problems that are inherently embedded in history and context are reduced to problems that can be measured and quantified Data is necessarily always about something and never about an abstract entity.

The collection, analysis, and manipulation of data potentially entails monitoring, tracking, and surveilling people. This necessarily impacts people directly or indirectly whether it manifests as change in their insurance premiums or refusal of services. The erasure of the person behind each data point makes it easy to “manipulate behavior” or “nudge” users, often towards profitable outcomes for companies. Considerations around the wellbeing and welfare of the individual user, the long-term social impacts, and the unintended consequences of these systems on society’s most vulnerable are pushed aside, if they enter the equation at all. For companies that develop and deploy AI, at the top of the agenda is the collection of more data to develop profitable AI systems rather than the welfare of individual people or communities. This is most evident in the FinTech sector, one of the prominent digital markets in Africa. People’s digital footprints, from their interactions with others to how much they spend on their mobile top ups, are continually surveyed and monitored to form data for making loan assessments. Smartphone data from browsing history, likes, and locations is recorded forming the basis for a borrower’s creditworthiness.

Artificial Intelligence technologies that aid decision-making in the social sphere are, for the most part, developed and implemented by the private sector whose primary aim is to maximise profit. Protecting individual privacy rights and cultivating a fair society is therefore the least of their concerns, especially if such practice gets in the way of “mining” data, building predictive models, and pushing products to customers. As decision-making of social outcomes is handed over to predictive systems developed by profit-driven corporates, not only are we allowing our social concerns to be dictated by corporate incentives, we are also allowing moral questions to be dictated by corporate interest.

“Digital nudges”, behaviour modifications developed to suit commercial interests, are a prime example. As “nudging” mechanisms become the norm for “correcting” individuals’ behaviour, eating habits, or exercise routines, those developing predictive models are bestowed with the power to decide what “correct” is. In the process, individuals that do not fit our stereotypical ideas of a “fit body”, “good health”, and “good eating habits” end up being punished, outcast, and pushed further to the margins. When these models are imported as state-of-the-art technology that will save money and “leapfrog” the continent into development, Western values and ideals are enforced, either deliberately or intentionally….(More)”.