COMPUTATIONAL EQUIVALENCE: A STRUCTURED LAB METHODOLOGY FOR COMPARATIVE LAW IN THE AGE OF ARTIFICIAL INTELLIGENCE

Abstract

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Comparative legal scholarship faces a fundamental challenge of scale as the global volume of case law and statutory data expands exponentially. Traditional manual methodologies are limited by human processing capacity, creating a critical bottleneck for empirical legal studies. This article proposes a novel, algorithmic framework for defining "Legal Equivalence" designed to structure human analysis and train computational systems. Building on a four-level spectrum of equivalence—Total, Functional, Partial, and No Direct Equivalent—this framework moves beyond binary distinctions by introducing a high-resolution 31-point scale (0.0–3.0) to establish a nuanced, data-driven classification system.

This decimal-based metric (d) allows for the calculation of Legal Distance, creating a precise data layer that quantifies operational friction and reliability gaps, enabling researchers to distinguish between "Strong" and "Weak" functional equivalents based on empirical performance. The article introduces a standardized decision tree that converts abstract doctrinal analysis into structured data, enabling the application of Large Language Models (LLMs) and Natural Language Processing (NLP) to comparative research. To address AI safety, it introduces a "Dual-Protocol" for Null Values, preventing hallucination in drafting while preserving data integrity for analytics.

The methodology details specific empirical protocols for resolving "border cases," including "Feature Mapping" to deconstruct partial equivalents, "Statistical Outcome Analysis" to verify functional equivalents via dataset reliability, and "Professional Consensus Verification" (utilizing expert heuristics as falsifiable Bayesian priors to resolve data voids). To ensure doctrinal integrity, the methodology integrates a "Scholarly Authentication" protocol—a rigorous Human-in-the-Loop audit that verifies AI-generated data points against the pillars of accuracy, contextual synthesis, and ethical accountability. Finally, the framework extends this logic to the dimension of time, introducing a Legal Convergence Vector (V_legal) that utilizes the decimal scale to track the magnitude and direction of "Legal Convergence" over time. By applying a single invariant metric (d) to both jurisdictional difference (space) and historical evolution (time), this methodology establishes a unified coordinate system for law—conceptually analogous to a general theory of relativity for legal dynamics. This unification transforms the field from anecdotal observation to empirical calibration, offering a blueprint for the future of computational comparative law.

Methodology Note: Working Paper (v3.0)

This is a Working Paper and "Human-in-the-Loop" (HITL) algorithmic framework intended for community feedback. It seeks to calibrate the scale of scholarly judgment by delegating less ambiguous cases to computational systems while isolating complex doctrinal ambiguities for deep human interpretation. Comments are welcome at jckingattorney@gmail.com.