AUTHOR=Qi Yin , Zhao Zihan 

TITLE=Ethical challenges in scene understanding for public health AI

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1685813

DOI=10.3389/fpubh.2025.1685813

ISSN=2296-2565

ABSTRACT=IntroductionIntegrating 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.MethodsTo 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 admissibility into both representation and decision processes. Moreover, a dynamic conflict-resolution mechanism, reflective equilibriumstrategy, is developed to adapt policy behavior in response to evolving ethical considerations, facilitating principled moral deliberation under uncertainty. This dual-structured approach, combining embedded normative templates with adaptive strategic mechanisms, ensures that AI behaviors align with public health values such as transparency, accountability, and privacy preservation.Results and discussionExperimental evaluations reveal that the framework achieves superior ethical alignment, reduced norm violations, and improved adaptability compared to traditional constraint-based systems. By bridging formal ethics, machine learning, and public interest imperatives, this work establishes a foundation for deploying ethically resilient AI in public health scenarios demanding trust, legality, and respect for human dignity.