Ethical challenges in scene understanding for public health AI

Introduction Integrating AI into public health introduces complex ethical challenges, especially in scene understanding, where automated decisions affect socially sensitive contexts. In contexts requiring heightened sensitivity, including disease surveillance, patient monitoring, and behavioral analysis, the interpretability, fairness, and accountability of AI systems are crucial parameters. Conventional approaches to ethical modeling in AI often impose normative concerns as external constraints, resulting in post-hoc evaluations that fail to address ethical tensions in real time. These deficiencies are especially problematic in public health applications, where decision making must safeguard privacy, foster social trust, and accommodate diverse moral frameworks. Methods To address these limitations, this study introduces a methodological framework that integrates ethical reasoning into the learning architecture itself. The proposed model, VirtuNet, incorporates deontic constraints and stakeholder preferences within its computational pathways, embedding ethical adm