Efficiently Transforming Neural Networks into Decision Trees: A Path to Ground Truth Explanations with RENTT

The introduction of the RENTT algorithm marks a significant advancement in the field of explainable AI by transforming neural networks into decision trees, which are more interpretable and trustworthy. Neural networks, while powerful, have been criticized for their black-box nature, leading to a lack of trust in their decisions. Existing explainable AI methods often fail to provide faithful explanations, which can misalign with the actual decision-making logic of the neural networks. RENTT addresses these challenges by ensuring that the decision tree representation is exact, scalable, and interpretable, even for complex neural network architectures. This transformation not only enhances the clarity of AI decisions but also provides a method to calculate ground truth feature importance, further solidifying the reliability of AI systems. The implications of this research could lead to broader acceptance and integration of AI technologies across various sectors.