Dependency, Data and Decolonisation: A Framework for Decolonial Thinking in Collaborative AI Research

TL;DR

This paper proposes a decolonial framework for collaborative AI research, analyzing neo-colonial practices in academia and illustrating it through a case study of a low-cost air pollution sensor project in Uganda.

Contribution

It introduces a decolonial thinking framework for AI research and demonstrates its application through a case study of the AirQo project in Uganda.

Findings

Identifies neo-colonial practices in Western academia

Develops a decolonial framework for AI research

Showcases a successful low-cost sensor network case study

Abstract

This essay seeks to tie together thoughts on the political economy of academia, the inequities in access to the academic means of production and decolonial practice in data empowerment. To demonstrate this I will provide a brief analysis of the neo-colonial, extractive practices of the Western Academy, introduce concepts around decolonial AI practice and then use these to form an investigative framework. Using this framework, I present a brief case study of the AirQo project in Kampala, Uganda. The project aims to deploy a low-cost air pollution sensor network across the city, using machine learning methods to calibrate these sensors against reference instruments, providing high-quality air pollution data at a far lower cost.