2025/01/01 by Prescher, Timothy J
Arts and Humanities · Computer Science · Engineering · Neuroscience · Social Sciences · #Adversarial Robustness in Machine Learning #Algorithmic Ethics #Artificial Intelligence #Artificial Intelligence and Robotics #Arts and Humanities #Autonomous Systems #Computational Modeling #Computer Engineering #Computer Sciences #Decision Theory #Engineering #Epistemic Humility #Ethical Governance #Ethics and Political Philosophy #Ethics and Social Impacts of AI #FOS: Philosophy #Formal Systems #Free Will and Agency #Human–AI Collaboration #Knowledge Frameworks #Mathematical Ethics #Metaethics #Moral Calibration #Moral Philosophy #Moral Responsibility #Operational Research #Operations Research #Other Computer Engineering #Philosophy #Philosophy of Mind #Philosophy of Science #Physical Sciences and Mathematics #Proportional Duty #Systems Engineering #Systems Engineering and Industrial Engineering #Systems Ethics #Uncertainty #ethics and religion
paper · doi:10.17605/osf.io/hn7kc
openalex publication_date 2025/01/01 · openalex created_date 2025/12/01 · openalex updated_date 2026/07/15
This project presents the Principle of Proportional Duty (PPD), a mathematically formalized ethical framework that links knowledge, humility, and contextual coherence to moral duty. It explores how proportional calibration improves decision stability under high-uncertainty conditions. The project includes theoretical development, computational validation, and applied integration with AI management standards such as ISO/IEC 42001.