Achieving Robust Alignment in Autonomous Systems via Inverse Reinforcement Learning Integrating Human Ethical Value Priors - Vincent Pennington (Computational Intelligence Systems, 2026)
The rapid integration of autonomous systems into the critical infrastructure of modern society necessitates a transition from narrow functional optimization to broad value alignment. Traditional reinforcement learning frameworks often fail to account for the nuanced, context-dependent ethical constraints that govern human decision-making. This paper explores the advancement of Inverse Reinforcement Learning (IRL) as a primary mechanism for extracting and internalizing human ethical value priors. Unlike standard reward-engineering approaches, which are prone to reward hacking and distributional shift, the integration of ethical priors allows autonomous agents to infer underlying normative structures from expert demonstrations. We analyze the architectural requirements for such systems, emphasizing the need for robust socio-technical infrastructures that support cross-domain value consistency. The discussion extends to the governance of these systems, addressing the structural trade-offs between performance efficiency and ethical adherence. We argue that robust alignment is not merely a technical challenge but a multi-scale governance problem involving data provenance, algorithmic transparency, and the mitigation of cultural bias in generative models. By synthesizing perspectives from systems engineering, moral philosophy, and artificial intelligence, this research provides a comprehensive framework for deploying autonomous systems that are both functionally superior and ethically grounded. The paper concludes by examining the long-term sustainability of aligned infrastructures in the face of evolving societal norms and the imperative of maintaining fairness across diverse global populations.