AI and Evaluation: Exploring Made in Africa Approaches for Transformational Practice
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Abstract
Artificial intelligence (AI) presents both transformational opportunities and challenges for evaluation practice in an era of interconnected global crises. This article examines how evaluators can engage AI in ways that align with the transformational imperative and decolonial commitments, rather than reproducing extractive or exclusionary practices. The central questions guiding this inquiry are: How can AI technologies be adopted within evaluation practice without reproducing colonial patterns of extraction and dominance? What essential infrastructures and competencies do evaluators need to integrate AI systems responsibly within transformational agendas? Drawing on “Made in Africa Evaluation” (MAE) and “Made in Africa AI” (MAAI) as intersecting movements, the article examines tensions between growing interest in AI and fundamental concerns about current AI systems. Environmental costs (energy and water use), extractive labour arrangements, and Western-biased datasets raise questions about alignment with transformational imperatives. The concentration of AI development within Western corporations further challenges decolonized evaluation efforts. Through a decolonial analysis of MAE and MAAI experiences, we show how these movements clarify both practical opportunities for AI-enabled evaluation and the risks of reinforcing existing power asymmetries. Together, they offer pathways toward AI adoption that centers African contexts, languages, and epistemologies while promoting equitable and sustainable evaluation practice.
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