Ethical and Human-Aligned Artificial Intelligence for Public Welfare, Financial Integrity, and Pediatric Healthcare Decision Systems

Abstract

Artificial intelligence increasingly governs decisions in public welfare administration, financial integrity systems, and pediatric healthcare, where errors, bias, or opacity can result in significant human harm. While advances in predictive modeling have improved efficiency and scale, insufficient ethical alignment, transparency, and human oversight continue to undermine trust and legitimacy. This research proposes an ethical, human-aligned artificial intelligence framework that integrates behavioral analytics, explainable decision modeling, trust calibration, and governance-aware controls across public-sector, financial, and healthcare environments. Drawing on prior work in autism behavioral prediction, IoT-enabled health monitoring, financial fraud detection, cybersecurity, human-centered AI, and ethical governance frameworks, the study develops a unified methodology for responsible AI deployment. Through cross-domain simulation and analytical evaluation, the framework demonstrates improved fairness, reduced false positives, enhanced interpretability, and stronger alignment with human judgment. The findings underscore the necessity of embedding ethics and human alignment as core architectural properties in AI systems operating within high-impact socio-technical domains.