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<p>The interdependence of human, animal, and environmental health underscores the necessity for integrated approaches, such as One Health, to address global health threats. In this context, responsible artificial intelligence (AI) holds substantial potential to enhance early warning systems and bolster community preparedness for disease outbreaks. However, achieving this potential, particularly in the Global South, requires more than just technical innovation; it demands inclusive and sustained community engagement, especially with populations that have historically been marginalized from technological development and decision-making. This article explores how the Global South AI for Pandemic and Epidemic Preparedness and Response (AI4PEP) Network is advancing a community-driven approach to AI in public health through participatory and culturally informed engagement frameworks. Drawing on AI4PEP initiatives from African countries, this examination focuses on inclusive strategies, such as stakeholder mapping, adapting engagement formats to local contexts, and integrating lived experiences to empower community agencies in shaping AI systems. Findings reveal a shared commitment across contexts to co-create AI tools that reflect local realities, empower marginalized voices, and foster social legitimacy. Trust-building emerges as both a prerequisite and a result of equitable engagement. Furthermore, the article emphasizes the importance of researcher positionality, highlighting the need for reflexivity as researchers navigate their roles as facilitators, brokers, and co-learners in complex sociopolitical landscapes. Ultimately, the article advocates for merging research and implementation, rethinking predominant ethical and governance frameworks, and centering epistemic justice. It calls for a shift toward AI systems that are not only technically robust but also socially grounded, responsive, and just.</p>

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Reimagining Artificial Intelligence for zoonotic disease detection in Africa: a decolonial approach rooted in community engagement and local knowledge

  • 期刊论文

  • 发表日期：

    2025年6月28日

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  • Sylvain Landry Birane Faye,
  • Denis Nkweteyim,
  • Georgette Helene Coumba Sow,
  • Boly Diop,
  • Fatoumata Bintou Diongue,
  • Birane Cisse,
  • Nsagha Dickson,
  • Ndiaye Dia,
  • Kingsley Badu,
  • Gelan Ayana,
  • Mouhamadou Lamine Ba,
  • Rose-Mary Owusuaa Mensah Gyening,
  • Moise Ondua,
  • Fatou Ndiaye,
  • Massamba Diouf,
  • Rudith King,
  • Vincent Duclos,
  • Emmanuel Musa,
  • Mary Goitom,
  • Bruce Mellado,
  • Jude Kong

摘要

The interdependence of human, animal, and environmental health underscores the necessity for integrated approaches, such as One Health, to address global health threats. In this context, responsible artificial intelligence (AI) holds substantial potential to enhance early warning systems and bolster community preparedness for disease outbreaks. However, achieving this potential, particularly in the Global South, requires more than just technical innovation; it demands inclusive and sustained community engagement, especially with populations that have historically been marginalized from technological development and decision-making. This article explores how the Global South AI for Pandemic and Epidemic Preparedness and Response (AI4PEP) Network is advancing a community-driven approach to AI in public health through participatory and culturally informed engagement frameworks. Drawing on AI4PEP initiatives from African countries, this examination focuses on inclusive strategies, such as stakeholder mapping, adapting engagement formats to local contexts, and integrating lived experiences to empower community agencies in shaping AI systems. Findings reveal a shared commitment across contexts to co-create AI tools that reflect local realities, empower marginalized voices, and foster social legitimacy. Trust-building emerges as both a prerequisite and a result of equitable engagement. Furthermore, the article emphasizes the importance of researcher positionality, highlighting the need for reflexivity as researchers navigate their roles as facilitators, brokers, and co-learners in complex sociopolitical landscapes. Ultimately, the article advocates for merging research and implementation, rethinking predominant ethical and governance frameworks, and centering epistemic justice. It calls for a shift toward AI systems that are not only technically robust but also socially grounded, responsive, and just.