Epistemic decolonisation and the integration of African traditional medicine: toward an epistemic redress model for inclusive AI in mental healthcare

Artificial intelligence (AI), particularly machine learning (ML) systems, is increasingly used in mental healthcare to support diagnosis and treatment. These systems analyse large datasets—including speech patterns, social media activity, and biometric indicators—to detect conditions such as depression and anxiety. Although often framed as objective and clinically neutral, these technologies embed epistemic and cultural assumptions derived largely from Global North contexts. This paper advances a decolonial critique of AI in mental healthcare by arguing that ML systems reproduce colonial epistemologies through their underlying data practices, categories, and validation structures. Drawing on Fricker’s (2007) account of epistemic injustice and Dotson’s (2012) theory of epistemic oppression, I argue that the exclusion of Indigenous knowledge systems (IKS)—particularly African traditional medicine (ATM)—from AI design and implementation constitutes both testimonial and hermeneutical injustice. Building on this critique, the paper develops a preliminary Epistemic Redress Model for Mental Health AI (Sect. 5), which outlines the epistemic conditions required for more inclusive and context-sensitive machine learning systems. Using South Africa as a case study, I demonstrate how ATM offers alternative conceptual and therapeutic resources for understanding mental illness that are systematically marginalised within dominant biomedical and data-driven paradigms. The paper concludes that without structured epistemic redress, AI systems in mental healthcare risk reinforcing colonial hierarchies of knowledge and producing ethically and clinically inadequate models of care.