03100nas a2200433   4500000000100000008004100001260004400042653002800086653002500114653002200139653001500161653001700176653001900193100001300212700001600225700001200241700001100253700001500264700001200279700001400291700001000305700001100315700001200326700001000338700002200348700001200370700001300382700001200395700001100407700001300418700001100431700001300442700001400455700001100469245015500480856006500635520194100700022002502641       2025                            d  bSpringer Science and Business Media LLC10aArtificial Intelligence10aCommunity engagement10aEpistemic justice10aOne Health10aGlobal south10aHuman-centered1 aFaye SLB1 aNkweteyim D1 aSow GHC1 aDiop B1 aDiongue FB1 aCisse B1 aDickson N1 aDia N1 aBadu K1 aAyana G1 aBa ML1 aMensah Gyening RO1 aOndua M1 aNdiaye F1 aDiouf M1 aKing R1 aDuclos V1 aMusa E1 aGoitom M1 aMellado B1 aKong J00aReimagining Artificial Intelligence for zoonotic disease detection in Africa: a decolonial approach rooted in community engagement and local knowledge  uhttps://link.springer.com/article/10.1007/s43681-025-00779-53 a<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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