Creating Bayesian Ethical Alignment Models for Eliciting, Modeling, and Calibrating Ethical Human Decision-Making Values and Priorities

Ethical decision-making in artificial intelligence (AI) is inherently complex, context-dependent, and influenced by competing priorities. This paper presents a Bayesian ethical alignment modeling approach designed to capture, model, and calibrate human ethical values for AI decision-making. Unlike rule-based approaches, our method encodes organizational ethics within a probabilistic framework, allowing for dynamic updates and structured reasoning. We outline a structured methodology for elicitation, calibration, and validation, tested in high-stakes scenarios. We posit that this framework would enable AI to align with human ethical priorities while maintaining adaptability, scalability, and transparency.