CHiQPM: Calibrated Hierarchical Interpretable Image Classification

Interpretability doesn't require sacrificing accuracy: CHiQPM maintains 99% of the accuracy of standard non-interpretable models while providing detailed explanations, challenging the common assumption that explainability comes at a significant performance cost.

Hierarchical explanations mirror human reasoning: The model organizes its decision-making in hierarchical layers that better reflect how human experts naturally think through problems, making AI reasoning more intuitive for domain specialists to follow and verify.

Dual-level explanations support better human-AI collaboration: By combining global explanations (why the model favors certain classes overall) with local explanations (why it made a specific decision), CHiQPM provides complementary information that helps human experts catch errors and understand edge cases more effectively.

Built-in uncertainty quantification improves trustworthiness: The model's integrated conformal prediction method provides interpretable confidence measures alongside predictions, allowing safety-critical applications to know not just what the model decided, but how confident it should be—crucial for domains like medical imaging or autonomous systems.

Practical for safety-critical domains: The combination of maintained accuracy, interpretable reasoning, and calibrated uncertainty makes this approach particularly valuable for high-stakes applications where both performance and explainability are regulatory or ethical requirements.