AI-AUGMENTED JUDICIAL RELATIVITY FRAMEWORK (JRF) AN EXPLAINABLE, DIMENSIONALITY-REDUCING DECISION-SUPPORT METHODOLOGY FOR AUGMENTED JUSTICE

Modern judicial systems must synthesize numerous interconnected variables — statutory provisions, factual evidence, intent, witness credibility, forensic findings, constitutional principles, and historical precedent — while remaining consistent and unbiased. This paper proposes the Judicial Relativity Framework (JRF), an AI-assisted decision-support methodology inspired by Einstein\'s concept of multiple frames of reference and by dimensionality-reduction principles from machine learning. JRF transforms a complex legal problem into a small number of interpretable decision dimensions — factual certainty, legal similarity, ethical impact, constitutional compatibility, and precedent consistency — without replacing judicial discretion. It combines explainable artificial intelligence, legal knowledge graphs, semantic precedent retrieval, causal inference, and evidence weighting to produce a transparent judicial-assistance report; its dimensionality-reduction, semantic-embedding, contradiction-detection, precedent-retrieval, and explainable-attribution components are given a full mathematical formalization, so every quantitative claim is precisely defined rather than merely illustrated. The resulting judicial dimensions are exact, traceable linear combinations of the underlying evidentiary features, and their contributing evidence can be recovered through an exact Shapley-value decomposition. Two illustrative case studies — a property-ownership dispute and a fatal road accident — show that the same architecture and mathematics generalize across areas of law, while judges retain complete authority over the final verdict. By reframing legal disputes as a small set of interpretable, mathematically grounded dimensions rather than a mass of correlated variables, JRF offers augmented rather than automated justice, improving consistency and transparency subject to fairness, explainability, and due-process safeguards. The framework remains a conceptual and mathematical proposal; empirical validation is identified as necessary future work.