A Probabilistic Artificial Intelligence Model for Quantification and Prioritization of Global Human Suffering

This paper proposes a Real-Time AI Framework for Human Suffering Quantification and Prioritization designed to support structured humanitarian decision-making across multiple global domains. The framework integrates multi-sector indicators of human suffering, including water stress, food insecurity, healthcare access, housing deficits, economic inequality, environmental degradation, governance instability, and conflict exposure, into a unified probabilistic prioritization model. Empirical context indicates the global scale of these conditions. For example, with approximately 25 countries experiencing extreme water stress and over 100 countries facing varying levels of water insecurity. Food insecurity affects 50-60 countries at significant levels, while over 700 million people are undernourished globally. Healthcare system gaps are present in approximately 90-110 countries, and housing deficits impact an estimated 80-100 countries, with over 1.6-2 billion people living in inadequate housing conditions worldwide. Corruption, economic inequality, and public debt are near-universal structural conditions across sovereign states, while environmental pressures such as climate change and biodiversity loss affect nearly all countries globally. Active armed conflict is concentrated in approximately 20-30 countries, with broader instability affecting more than 50 countries. The proposed model normalizes these heterogeneous indicators into a unified Human Suffering Index and applies probabilistic weighting to generate a dynamic Priority Action Score for each region. The system is designed to operate in real time or near real time using institutional datasets from organizations such as the United Nations, the World Health Organization, and the World Bank. The framework is platform-agnostic and intended for integration into AI-based decision-support systems for humanitarian analysis, risk assessment, and global resource prioritization. Ethical constraints, data transparency requirements, and probabilistic uncertainty handling are embedded to ensure responsible application. This work establishes a structured foundation for AI-assisted quantification of human suffering and scalable prioritization of global humanitarian needs.