Data Governance for Lifelong Intelligent Systems in Health and Disaster Adaptation

Abstract

Lifelong intelligent systems require scalable data governance architectures that protect privacy, support adaptation, and maintain reliability across changing environments. This entry connects privacy-aware learning systems with adversarial security, sequential recommendation, biomedical injury modeling, and flood disaster adaptation. Universal transferable attacks highlight the need for robust model governance, while LLM-based sequential recommendation illustrates the importance of dynamic user representation and continual updating. Biomedical evidence on hepatic ischemia-reperfusion and remote cardiac injury shows that intelligent systems may need to manage complex causal and mechanistic data. Flood relocation research provides a governance-intensive disaster setting where household heterogeneity, relocation incentives, and public policy interact. This literature cluster supports the development of intelligent learning systems that combine privacy protection, adversarial robustness, adaptive recommendation, biomedical evidence integration, and disaster-oriented decision support.