Unpacking the Ethical Value Alignment in Big Models

Big models embed hidden ethical values that may not align with society's diverse moral perspectives. The paper uses Moral Foundation theory to analyze mainstream Large Language Models (LLMs) and finds that these models have inherent moral inclinations that aren't always transparent to users—meaning an AI system trained by one organization may reflect that organization's particular ethical worldview rather than universal values.

Current AI ethics guidelines are fragmented and lack universal agreement. The paper surveys existing AI ethics frameworks and finds they are inconsistent across organizations and regions, creating confusion about what "ethical AI" actually means. The authors argue for a unified, collaborative approach to establishing shared ethical standards rather than allowing each company to define ethics independently.

Aligning AI systems with ethical values is technically complex and poorly understood. The paper examines existing alignment algorithms (methods used to make AI systems behave ethically) and identifies significant gaps in how well these methods work. Current approaches struggle with the fundamental challenge of translating abstract ethical principles into concrete technical solutions.

We need new ways to evaluate whether AI systems are actually ethically aligned. The paper highlights that there are no agreed-upon standards for measuring whether a big model truly reflects ethical values or just appears to on the surface. Better evaluation methods are essential before deploying these systems in high-stakes decisions affecting people's lives.

Solving AI ethics requires collaboration across academia, industry, and society—not just technologists. The authors emphasize that establishing ethical AI cannot be a purely technical problem solved by engineers alone; it requires input from ethicists, social scientists, policymakers, and affected communities to create frameworks that reflect diverse human values.