Rules, Cases, and Reasoning: Positivist Legal Theory as a Framework for Pluralistic AI Alignment

TL;DR

This paper proposes using legal theory, specifically the interaction of rules and cases, as a framework for AI alignment to handle pluralism and vague principles while maintaining democratic values.

Contribution

It introduces a novel legal-theoretic framework for AI alignment that emphasizes the role of rules and cases in balancing convergence and pluralism.

Findings

Legal theory offers solutions for specifying vague principles.

Rules and cases facilitate convergence on meaning while respecting disagreement.

Applying legal frameworks can improve democratic AI alignment processes.

Abstract

Legal theory can address two related key problems of alignment: pluralism and specification. Alignment researchers must determine how to specify what is concretely meant by vague principles like helpfulness and fairness and they must ensure that their techniques do not exclude alternative perspectives on life and values. The law faces these same problems. Leading legal theories suggest the law solves these problems through the interaction of rules and cases, where general rules promulgated by a democratic authority are given specific content through their application over time. Concrete applications allow for convergence on practical meaning while preserving space for disagreement on values. These approaches suggest improvements to existing democratic alignment processes that use AI to create cases that give content to rules, allowing for more pluralist alignment.