Perspective on ethical AI in computational epidemiology and one health with COHRCIE framework and computational prophylaxis

Abstract

The increasing use of artificial intelligence (AI) in Computational Epidemiology and One Health (CEOH) raises complex ethical challenges related to transparency, fairness, privacy, and legal compliance. This study examines the integration of computational science with traditional epidemiology to address health issues affecting humans, animals, and the environment. We trace the evolution of computational epidemiology and explore how machine learning and AI are applied in predictive modeling, disease surveillance, personalized medicine, resource allocation, and environmental monitoring. As AI transforms the landscape of CEOH, new ethical concerns continue to emerge. To address these concerns, we introduce the concept of computational prophylaxis, which enhances traditional disease prevention, and AI as a computational prophylactic tool. We also propose the COHRCIE framework, an ethical roadmap designed to ensure accuracy, transparency, integrity, privacy, equitable access, and responsible data governance in AI-driven CEOH initiatives. By promoting anticipatory ethics and embedding compliance throughout the AI lifecycle, COHRCIE provides a practical structure for building trustworthy, inclusive, and transparent AI systems in both research and applied health settings.