Rules and Revelations: Balancing Interpretability and Flexibility in NLP Approaches to Legislative Design Analysis

This paper explores the potential and challenges of applying natural language processing (NLP) tools to analyze legislative design in democratic systems, focusing on the trade-offs between rule-based methods and machine-learning approaches, particularly large language models (LLMs). Legislative design is conceptualized through two key dimensions — versatility (breadth of topics, addressees, and policy tools) and precision (clarity, specificity, and conditions of applicability) — each posing distinct challenges for computational analysis. This study contrasts rule-based and machine-learning methods, emphasizing the difficulty of aligning theoretical legal constructs with the complex, often opaque outputs generated by LLMs. Rule-based approaches, grounded in predefined linguistic patterns, offer transparency and traceability, which align closely with theoretical legal frameworks by enabling researchers to map textual features directly to legislative concepts. This transparency is essential for legal analysis, facilitating precise validation and ensuring consistency with established legal standards. Rule-based methods are particularly effective in identifying explicit features, such as exemptions and delegation clauses, where straightforward patterns reflect clear theoretical constructs. However, these methods struggle to capture the implicit meanings, contextual nuances, and evolving terminologies that characterize legislative language, potentially limiting their capacity to fully address the multi-dimensional nature of legislative design. In contrast, machine-learning models, particularly LLMs, demonstrate the ability to capture latent semantic relationships and handle the nuanced, context-sensitive nature of legislative texts. Despite these strengths, the opaque or "black-box" nature of LLMs presents notable obstacles in legal analysis. Unlike rule-based approaches, LLMs process text through complex, often non-intuitive patterns that resist clear interpretation, complicating the validation process and raising concerns about the reliability and accountability of machine-learning outputs in legal contexts. For instance, an LLM’s classification of legislation as “precise” or “versatile” may rely on patterns not readily apparent to researchers, risking interpretations that could diverge from foundational legal principles. This paper proposes a hybrid framework that combines the interpretability of rule-based methods for capturing structured, explicit features with the flexibility of machine-learning models for identifying latent, contextually complex dimensions of legislative texts. Additionally, it serves as a practical guide for determining when specific measurement approaches from the general NLP toolset are most suitable. This integrative approach provides a structured, interpretable pathway for examining legislative design in democratic systems, advancing methodological rigor and enhancing theoretical fidelity in computational legal analysis.