earticle

영어

Artificial General Intelligence (AGI) introduces a new class of ethical and technical challenges because it is expected to operate with autonomous goal formation, extended temporal reasoning, and reflective metacognition that go far beyond the constraints of current narrow AI systems. These capabilities imply that ethical safeguards cannot remain external layers or post-hoc filters; instead, they must function as internal cognitive components embedded within the AGI’s core architecture. To address this need, this paper proposes a Modular Ethical AGI Framework composed of three foundational subsystems: a Hybrid Alignment Stack that unifies top-down normative principles with bottom-up, data-driven moral priors; a Moral Reflection Module capable of contextual ethical assessment, symbolic interpretation, and counterfactual reasoning; and a Metacognitive Consistency Layer that performs coherence evaluation, reflective self-correction, and justification generation. To operationalize these subsystems, we introduce an Ethical Deliberation Cycle, which provides a structured sequence for moral feature extraction, normative activation, action evaluation, conflict resolution, reflective consistency checking, and explanation generation. This framework directly addresses limitations widely observed in current alignment research, including rule brittleness [1], lack of contextual nuance [2], dataset bias [3], and absence of principled coherence mechanisms [4]. It further identifies potential failure modes such as value–rule conflicts, cultural narrowness in moral datasets, symbolic grounding gaps, and metacognitive overconfidence. We argue that ethical reasoning is not an optional enhancement but a structural necessity for AGI safety, and that the proposed modular architecture offers a viable starting point for designing trustworthy and value-aligned autonomous intelligence.