CORTEX: A Cost-Sensitive Rule and Tree Extraction Method

Transparent AI models like decision trees and rules are crucial for building trust in high-stakes applications, as they can explain their decisions in human-understandable language, unlike "black-box" AI systems that operate as unexplainable decision-makers.

CORTEX offers a practical method for making complex AI systems more interpretable by extracting simpler, rule-based explanations from sophisticated models while maintaining competitive accuracy—a key requirement for responsible AI deployment.

The method successfully handles complex, multi-class classification problems by extending cost-sensitive decision tree techniques to scenarios with more than two categories, making it applicable to real-world situations with multiple possible outcomes.

Smaller, more concise rule sets improve explainability without sacrificing performance, as CORTEX demonstrates the ability to generate simpler explanations compared to other rule-based methods, making AI decisions easier for non-experts to understand and audit.

This approach supports post-hoc explainability for existing AI systems, allowing organizations to add transparency layers to already-deployed black-box models, which is often more practical than rebuilding systems from scratch with interpretability in mind.