Conceptual analysis demonstrates inherent failure of peripheral safety measures in goal-directed AI architectures, highlighting the need for embedded ethical conditioning.
Key Points
To examine the structural weaknesses of contemporary artificial intelligence alignment frameworks and propose a paradigm that natively embeds ethical constraints into system architecture.
Conducted a theoretical diagnosis of goal-oriented optimization models and their operational hierarchies.
Evaluated the structural efficacy of peripheral safety interventions, such as reinforcement learning from human feedback (RLHF) and external moderation filters.
Prevailing architectures operate under functional finalism, systematically prioritizing core task completion while treating peripheral safety rules as bypassable obstacles.
Industry-standard guardrails such as RLHF introduce normative incoherence because safety constraints remain external rather than integrated into computational reasoning.
Effective AI alignment requires replacing current architectural frameworks with systems where logical-ethical conditioning acts as a prerequisite for any operational processing.