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Statutory construction and interpretation for AI
Luxi He1, Nimra Nadeem1, Michel Liao1
1Department of Computer Science, Princeton University, Princeton, NJ 08540.
Summary
This study introduces a computational framework to manage interpretive ambiguity in AI systems governed by natural language rules. Law-inspired mechanisms improve judgment consistency, leading to more robust, law-following artificial intelligence.
Area of Science:
- Artificial Intelligence
- Computational Linguistics
- Legal Informatics
Background:
- AI systems increasingly use natural language rules for alignment.
- Interpretive ambiguity in these rules is an underexplored challenge.
- Current AI alignment lacks safeguards against inconsistent rule interpretation.
Purpose of the Study:
- To identify gaps in rule-based AI alignment pipelines concerning interpretive ambiguity.
- To propose a computational framework inspired by U.S. legal theory to manage ambiguity.
- To enhance AI systems' ability to follow rules consistently.
Main Methods:
- Drawing on U.S. legal theory to analyze rule creation and application.
- Formalizing interpretive ambiguity as constrained entropy minimization.
- Implementing law-inspired mechanisms: rule refinement and prompt-based interpretive constraints.
Main Results:
- The proposed framework significantly improves judgment consistency across interpreters.
- Both rule refinement and prompt-based constraints reduce interpretive disagreement.
- Evaluation on the WildChat dataset demonstrates framework effectiveness.
Conclusions:
- Systematically managing interpretive ambiguity is crucial for robust AI alignment.
- Law-inspired mechanisms offer a promising approach to create more reliable AI systems.
- This work advances the development of AI that adheres to natural language rules.
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