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

TL;DR

This paper proposes using the Method of Wide Reflective Equilibrium (MWRE), a moral epistemology framework, to improve the ethical robustness and procedural legitimacy of Large Language Model (LLM) alignment techniques like Constitutional AI.

Contribution

It introduces MWRE as a novel framework for analyzing and enhancing LLM alignment, emphasizing dynamic revision and procedural legitimacy over existing methods.

Findings

MWRE offers a more ethically grounded approach to LLM alignment.

Current methods like CAI resemble MWRE but lack its iterative revision process.

MWRE provides a heuristic for developing more ethically justifiable AI systems.

Abstract

As large language models (LLMs) become more powerful and pervasive across society, ensuring these systems are beneficial, safe, and aligned with human values is crucial. Current alignment techniques, like Constitutional AI (CAI), involve complex iterative processes. This paper argues that the Method of Wide Reflective Equilibrium (MWRE) -- a well-established coherentist moral methodology -- offers a uniquely apt framework for understanding current LLM alignment efforts. Moreover, this methodology can substantively augment these processes by providing concrete pathways for improving their dynamic revisability, procedural legitimacy, and overall ethical grounding. Together, these enhancements can help produce more robust and ethically defensible outcomes. MWRE, emphasizing the achievement of coherence between our considered moral judgments, guiding moral principles, and relevant background theories, arguably better represents the intricate reality of LLM alignment and offers a more robust path to justification than prevailing foundationalist models or simplistic input-output evaluations. While current methods like CAI bear a structural resemblance to MWRE, they often lack its crucial emphasis on dynamic, bi-directional revision of principles and the procedural legitimacy derived from such a process. While acknowledging various disanalogies (e.g., consciousness, genuine understanding in LLMs), the paper demonstrates that MWRE serves as a valuable heuristic for critically analyzing current alignment efforts and for guiding the future development of more ethically sound and justifiably aligned AI systems.