Aligning Artificial General Intelligence with Human Values: A Unified Framework for Explainability, Ethical Reasoning, and Long-Term Safety

Abstract

ABSTRACT As artificial intelligence systems rapidly transition from domain-specific narrow models toward highly autonomous, generalized architectures, the fundamental challenge of alignment—ensuring artificial general intelligence (AGI) strictly adheres to human values, ethical norms, and intended operational boundaries—has become the paramount concern of modern AI safety. Existing safety paradigms, including reinforcement learning from human feedback (RLHF), constitutional fine-tuning, and mechanistic interpretability, offer essential preliminary protections but exhibit critical failure modes under distributional shifts, novel capability emergence, and complex multi-agent interactions. This paper presents a comprehensive review of current alignment methodologies and proposes a novel Unified AGI Alignment Framework that integrates real-time explainability, dynamic ethical reasoning engines, and provable long-term safety containment mechanisms. Through rigorous comparative analysis and evaluation across technical metrics—including reward hack resistance, interpretability latency, and decision boundary stability—we demonstrate how multi-tiered alignment protocols substantially reduce risk profiles while preserving computational efficiency. Our synthesis outlines critical research gaps, actionable implementation roadmaps, and normative governance frameworks required to transition theoretical AGI alignment into scalable engineering guarantees.

KEYWORDS: Artificial General Intelligence, AGI Alignment, AI Safety, Mechanistic Interpretability, Deontic Logic Engine, Reward Hacking, Value Learning, Autonomous Containment.