Interpretable AI Systems: From Theory to Practice

This paper presents a comprehensive framework for developing interpretable AI systems that can explain their decisions to stakeholders. The research bridges the gap between theoretical approaches to AI interpretability and practical implementation challenges.

The authors analyze various techniques for making AI systems more transparent and understandable, including feature attribution methods, counterfactual explanations, and human-centered design approaches.

The study also addresses the crucial balance between model complexity and interpretability, offering guidelines for when and how to prioritize explainability in AI systems.