🧠 Model Understanding and Generative Alignment

MUGA LAB Reference Series — October 2025

📄 Access

🧭 Abstract

This reference defines the foundational concepts of Model Understanding and Generative Alignment, two complementary dimensions of responsible AI.

  • Model Understanding focuses on interpreting and explaining the internal mechanisms of predictive models — how they learn, what patterns they capture, and why they make certain predictions.
  • Generative Alignment addresses how generative systems (text, image, or data models) can be aligned with human values, ethics, and intent.

Together, these frameworks support the design of interpretable, trustworthy, and human-aligned generative intelligence — the central research theme of MUGA LAB.

🧩 Key Concepts

  • Model Interpretability and Transparency
  • Feature Importance & Explainability Metrics
  • Human-in-the-Loop Generative Alignment
  • Robustness, Diversity, and Mode Coverage
  • Value-Integrated Learning Objectives

🧾 Citation

MUGA LAB (2025). Model Understanding and Generative Alignment. MUGA LAB Reference Series, October 2025.
https://lexmuga.github.io/mugalab/references/2025-model-understanding

BibTeX: ```bibtex @techreport{mugalab2025modelunderstanding, title = {Model Understanding and Generative Alignment}, author = , year = {2025}, month = {October}, institution = {Model Understanding and Generative Alignment Laboratory}, url = {https://lexmuga.github.io/mugalab/references/2025-model-understanding}, note = {MUGA LAB Reference Series} }