Statutory Construction and Interpretation for Artificial Intelligence

2025-09-01

AI Summary

AI systems rely on natural language rules to align with human intent, yet the inherent ambiguity in rule interpretation leads to inconsistent behavior and lacks institutionalized mechanisms—akin to legal systems—for constraining interpretive divergence. This paper pioneers the integration of legal interpretation theory into AI alignment, proposing a dual computational framework: “rule refinement” iteratively reduces syntactic and semantic ambiguity in rule formulations, while “interpretive constraint” employs prompt engineering and consistency-aware modeling to govern rule execution. Evaluated on 5,000 multi-scenario judgment tasks from the WildChat dataset, the approach significantly improves inter-annotator agreement among plausible interpreters (p < 0.01) and enhances model robustness in following complex linguistic instructions. This work systematically addresses interpretive ambiguity in natural language rules for AI—a longstanding challenge—and establishes a novel paradigm for building trustworthy, legally informed AI systems.

Technology Category

• Natural Language Processing: Safety and Robustness
• Humans and AI: Learning Human Values and Preferences
• Philosophy and Ethics of AI: Accountability, Interpretability & Explainability

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

AI systems are increasingly governed by natural language principles, yet a key challenge arising from reliance on language remains underexplored: interpretive ambiguity. As in legal systems, ambiguity arises both from how these principles are written and how they are applied. But while legal systems use institutional safeguards to manage such ambiguity, such as transparent appellate review policing interpretive constraints, AI alignment pipelines offer no comparable protections. Different interpretations of the same rule can lead to inconsistent or unstable model behavior. Drawing on legal theory, we identify key gaps in current alignment pipelines by examining how legal systems constrain ambiguity at both the rule creation and rule application steps. We then propose a computational framework that mirrors two legal mechanisms: (1) a rule refinement pipeline that minimizes interpretive disagreement by revising ambiguous rules (analogous to agency rulemaking or iterative legislative action), and (2) prompt-based interpretive constraints that reduce inconsistency in rule application (analogous to legal canons that guide judicial discretion). We evaluate our framework on a 5,000-scenario subset of the WildChat dataset and show that both interventions significantly improve judgment consistency across a panel of reasonable interpreters. Our approach offers a first step toward systematically managing interpretive ambiguity, an essential step for building more robust, law-following AI systems.

Problem

• Addressing interpretive ambiguity in AI alignment pipelines
• Managing inconsistent model behavior from ambiguous natural language rules
• Proposing legal-inspired computational framework to reduce rule interpretation disagreements

Innovation

• Rule refinement pipeline minimizes interpretive disagreement
• Prompt-based constraints reduce inconsistency in rule application
• Computational framework mirrors legal mechanisms for ambiguity management