XNNTab -- Interpretable Neural Networks for Tabular Data using Sparse Autoencoders

Neural networks can now be made interpretable for tabular data: XNNTab demonstrates that it's possible to build neural networks that are both highly accurate and understandable by humans, addressing a major barrier to AI adoption in fields like healthcare and finance where interpretability is legally or ethically required.

Sparse autoencoders break down complex AI decisions into human-readable concepts: The model uses sparse autoencoders to convert opaque neural network features into "monosemantic features"—individual components that represent single, understandable concepts—allowing stakeholders to see exactly why the AI made a specific prediction.

Interpretable AI doesn't require sacrificing performance: XNNTab achieves performance comparable to traditional "black box" neural networks while outperforming simpler interpretable models like decision trees and linear regression, suggesting the false choice between accuracy and explainability can be overcome.

This approach is specifically designed for real-world business data: Unlike many interpretability techniques developed for image or text data, XNNTab is built for tabular data—the most common format in enterprise applications—making it immediately relevant to organizations making high-stakes decisions.

Responsible AI deployment becomes more feasible: By enabling both strong predictive performance and human interpretability, XNNTab helps organizations meet growing regulatory requirements (like GDPR's right to explanation) while maintaining competitive model accuracy.