Stable and Interpretable Deep Learning for Tabular Data: Introducing InterpreTabNet with the Novel InterpreStability Metric

TL;DR

This paper introduces InterpreTabNet, a deep learning model for tabular data that improves accuracy and interpretability, along with a new metric, InterpreStability, to evaluate interpretability consistency.

Contribution

The paper presents InterpreTabNet with an enhanced attentive module for stability and a novel InterpreStability metric for consistent interpretability evaluation.

Findings

InterpreTabNet outperforms existing models in accuracy on various datasets.

The InterpreStability metric effectively measures interpretability robustness.

The approach advances explainable AI for critical decision-making.

Abstract

As Artificial Intelligence (AI) integrates deeper into diverse sectors, the quest for powerful models has intensified. While significant strides have been made in boosting model capabilities and their applicability across domains, a glaring challenge persists: many of these state-of-the-art models remain as black boxes. This opacity not only complicates the explanation of model decisions to end-users but also obstructs insights into intermediate processes for model designers. To address these challenges, we introduce InterpreTabNet, a model designed to enhance both classification accuracy and interpretability by leveraging the TabNet architecture with an improved attentive module. This design ensures robust gradient propagation and computational stability. Additionally, we present a novel evaluation metric, InterpreStability, which quantifies the stability of a model's interpretability. The proposed model and metric mark a significant stride forward in explainable models' research, setting a standard for transparency and interpretability in AI model design and application across diverse sectors. InterpreTabNet surpasses other leading solutions in tabular data analysis across varied application scenarios, paving the way for further research into creating deep-learning models that are both highly accurate and inherently explainable. The introduction of the InterpreStability metric ensures that the interpretability of future models can be measured and compared in a consistent and rigorous manner. Collectively, these contributions have the potential to promote the design principles and development of next-generation interpretable AI models, widening the adoption of interpretable AI solutions in critical decision-making environments.