Abstract
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.
| Original language | English |
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| Title of host publication | Proceedings - 2025 IEEE Conference on Artificial Intelligence, CAI 2025 |
| Pages | 1235-1238 |
| Number of pages | 4 |
| ISBN (Electronic) | 9798331524005 |
| DOIs | |
| State | Published - 2025 |
| Event | 3rd IEEE Conference on Artificial Intelligence, CAI 2025 - Santa Clara, United States Duration: May 5 2025 → May 7 2025 |
Publication series
| Name | Proceedings - 2025 IEEE Conference on Artificial Intelligence, CAI 2025 |
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Conference
| Conference | 3rd IEEE Conference on Artificial Intelligence, CAI 2025 |
|---|---|
| Country/Territory | United States |
| City | Santa Clara |
| Period | 5/5/25 → 5/7/25 |
Bibliographical note
Publisher Copyright:© 2025 IEEE.
Keywords
- divergence metrics
- document content analysis
- Human-AI alignment
- multi-dimensional evaluation
- text analysis concordance
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Science Applications
- Computer Vision and Pattern Recognition
- Information Systems and Management
- Modeling and Simulation