{
  "id": 10684,
  "url": "https://link.springer.com/article/10.1007/s00146-026-03182-8",
  "title": "Epistemic decolonisation and the integration of African traditional medicine: toward an epistemic redress model for inclusive AI in mental healthcare",
  "summary": "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 ",
  "authors": null,
  "category": "research",
  "topics": "privacy-surveillance,healthcare",
  "orgs": null,
  "regions": "africa",
  "published_at": "2026-07-15T00:00:00.000Z",
  "fetched_at": "2026-07-16T05:10:56.605Z",
  "source_slug": "x-ai-society",
  "source_name": "AI & Society",
  "source_homepage": "https://link.springer.com/journal/146",
  "ethics_ai_record_url": "https://ethics.ai/record/10684",
  "original_url": "https://link.springer.com/article/10.1007/s00146-026-03182-8",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}