A Quantitative Framework for Interpretability, Safety, and Cross-Cultural Meaning Dynamics in Large Language Models

Interpretability research has long centered on the question, “Why did the model do X?” Yet most evaluations are conducted in an open system that analyzes the model in isolation and omits the human interlocutor — the source of semantic framing, emotional state, and interpretive variability. A scientifically valid closed-system interpretability framework must include all causal contributors, making the human–model pair the true unit of analysis. This paper introduces the Meaning Alignment Index (MAI), the first quantitative framework that treats alignment as a joint function of human emotional geometry, semantic clarity, and model-level semantic stability. MAI integrates a prosodic-emotional driver (Pt–(VAD)), human entropy (He), and task drift (Td) into an empirically testable score that predicts when meaning will remain coherent—and when it will diverge—even under deterministic model behavior. By shifting the interpretability question from "Why did the model do this?" to "Why did this human–model system produce this output?" MAI establishes a foundation for safer, more transparent, and more realistic interpretability diagnostics. The framework provides closed-system experimental designs, measurable drift dynamics, and a practical path toward quantifying alignment before misinterpretations propagate into high-stakes settings. MAI is unique in that it (i) enforces a closed-systemview of AI safety that explicitly includes the human interlocutor, (ii) detects misalignment before downstream tasks or actions execute, and (iii) is not tied to Western or Eastern cultural priors, making it compatible with globally diverse value systems. This paper develops each of these distinctions and argues that no interpretability method can be complete without explicitly measuring how human emotional geometry systematically alters meaning. Illustrative failure modes that MAI captures include: documented real-world incidents in which AI systems misinterpreted ‘roleplay’ with violent consequences, as well as plausible but currently unmeasured linguistic fractures—such as a user requesting an essay about ‘rubber’ (eraser) and receiving culturally misaligned content. Traditional interpretability methods analyze models after failure but cannot prevent semantic fracture before consequences emerge. Safety failures can arise from meaning fracture without any malicious intent, policy violation, or moral wrongdoing. This positions MAI as a safety-relevant interpretability framework that does not rely on moral authority, policy enforcement, or normative judgment to prevent harm. Ultimately, MAI is needed because human emotional geometry and cultural context systematically alter meaning—and no interpretability method can be complete without measuring them.