Algorithmic Colonization of Africa

By Abeba Birhane. 2020

246 citations recorded by the source corpus.

We live in a world where technological corporations hold unprecedented power and influence. Technological solutions to social, political, and economic challenges are rampant. In the Global South, technology that is developed with Western perspectives, values, and interests is imported with little regulation or critical scrutiny. This work examines how Western tech monopolies, with their desire to dominate, control and influence social, political, and cultural discourse, share common characteristics with traditional colonialism. However, while traditional colonialism is driven by political and government forces, algorithmic colonialism is driven by corporate agendas. While the former used brute force domination, colonialism in the age of AI takes the form of ‘state-of-the-art algorithms’ and ‘AI driven solutions’ to social problems. Not only is Western-developed AI unfit for African problems, the West’s algorithmic invasion simultaneously impoverishes development of local products while also leaving the continent dependent on Western software and infrastructure. By drawing examples from various parts of the continent, this paper illustrates how the AI invasion of Africa echoes colonial era exploitation. This paper then concludes by outlining a vision of AI rooted in local community needs and interests.

Subfields: Business and International Management, Safety, Robustness & Interpretability

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Business and International Management

In Safety, Robustness & Interpretability

Citations in the corpus

Re-imagining Algorithmic Fairness in India and Beyond (2021, background): “Scholars have pointed to how neoliberal AI follows the technical architecture of classic colonialism through data extraction, impairing indigenous innovation, and shipping manufactured services back to the data subjects—among communities already prone to exploitation, under-development, and inequali”
Data and its (dis)contents: A survey of dataset development and use in machine learning research (2020, background): “This can become especially problematic and dangerous in the most sensitive contexts, such as personal finance information, medical data, or biometrics.(105) However, machine learning researchers developing such da-”
The Values Encoded in Machine Learning Research (2021, methodology): “Works such as [11, 46] critique AI, ML, and data using a decolonial lens, noting how these technologies replicate colonial power relationships and values, and propose decolonial values and methods.”
Handle with Care: Lessons for Data Science from Black Female Scholars (2020, background): “…Data for Black Lives Movement Pulse Check (https://d4bl.org/reports.html) or Abe-ba’s plea for ‘‘genuine care for the welfare of the marginalized,’’ 8 I can tell that there’s something in the data that these Black women see—they see pulsing hearts, like their own, tragically twisted into…”
Decolonising Speech and Language Technology (2020, background): “It sits well with our belief in technologies as panaceas for social problems (Srinivasan, 2017; Birhane, 2020).”