Epistemic decolonisation and the integration of African traditional medicine
Toward an epistemic redress model for inclusive AI in mental healthcare
Bibliographic Data
| ID | 22417455 |
|---|---|
| Authors | Linda Maqutu (0000-0002-8516-1529, University of Johannesburg, corresponding author) |
| Year | 2026 |
| Publication date | 2026-07-15 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | AI & Society (JOURNAL) |
| Journal identifiers | ISSN: 0951-5666 • E-ISSN: 1435-5655 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s00146-026-03182-8 |
| OpenAlex | W7168396419 |
| Language | EN |
| References cited | 48 |
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.
Agency (philosophy) · Health care · Mental health · Mental healthcare · Mental illness · Redress · Transformative learning · African cultural and philosophical studies · Digital Mental Health Interventions · Race, Genetics, and Society
The DSM‐5
Integrating clients’ religion and spirituality within psychotherapy
Epistemologies of the South
Thinking about mental health and spirituality from the Indigenous knowledge systems frame of reference
Designing AI for mental health diagnosis
Why Epistemic Decolonisation in Africa
The historical relationship between African indigenous healing practices and Western-orientated biomedicine in South Africa
Conceptualizing Epistemic Oppression
The Fourth Industrial Revolution, Techno-Colonialism, and the Sub-Saharan Africa Response
The Technological Embodiment of Colonialism in Puerto Rico
Challenges and opportunities of centring the African voice in disability research
Voices of African Traditional Healers
Conceptual decolonization as an imperative in contemporary African philosophy
Relational Knowing and Epistemic Injustice
Can artificial intelligence be decolonized
African witchdoctors’ and popular culture
A Cautionary Tale
African traditional healing and mental well-being
Just pluralism
Epistemic Injustice in the Medical Context
Epistemic oppression, resistance, and resurgence
Health Problems
What is Indigenous Knowledge
| Citation velocity | historical |
|---|---|
| Highly cited | No |