Wide Reflective Equilibrium in LLM Alignment: Bridging Moral Epistemology and AI Safety

Current AI alignment methods like Constitutional AI have structural similarities to a proven moral philosophy approach — the Method of Wide Reflective Equilibrium (MWRE) — which balances specific moral judgments, general principles, and background theories to reach ethical coherence. This philosophical framework may better explain how LLM alignment actually works than simpler input-output evaluation models.

Most existing alignment techniques lack a crucial feature: two-way revision of principles. While Constitutional AI resembles MWRE structurally, it often fails to emphasize the dynamic, bidirectional process where both specific judgments and guiding principles can be revised together, which is central to MWRE's strength as a moral methodology.

Adopting MWRE could improve three key aspects of LLM alignment: dynamic revisability (the ability to update and improve over time), procedural legitimacy (ensuring the process is fair and transparent), and ethical grounding (having a solid philosophical foundation). These improvements could lead to more robust and defensible AI systems.

The paper acknowledges important limitations — LLMs lack consciousness and genuine understanding that humans possess — but argues MWRE still serves as a valuable framework for critically analyzing and improving how we align AI systems with human values, even given these differences.