Alignment Divergence Metrics: Measuring Human-AI Concordance in Document Content Analysis

This paper introduces the Alignment Divergence Metrics (ADM) framework quantifying concordance between human and AI analytical outputs in document analysis. We propose five dimensions for assessment: Content Identification, Thematic Organization, Interpretive Depth, Contextual Sensitivity, and Inferential Judgment, each with tailored mathematical formulations. ADM precisely identifies human-AI analytical divergence points, supporting targeted improvements. This standardized approach contributes to more reliable AI tools, responsible research deployment, empirically-grounded alignment theory, and informed policy development-addressing the gap in evaluations that typically focus on task performance rather than alignment with human analytical processes.