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As Artificial Intelligence (AI) integrates deeper into diverse sectors, thequest for powerful models has intensified. While significant strides have beenmade in boosting model capabilities and their applicability across domains, aglaring challenge persists: many of these state-of-the-art models remain asblack boxes. This opacity not only complicates the explanation of modeldecisions to end-users but also obstructs insights into intermediate processesfor model designers. To address these challenges, we introduce InterpreTabNet,a model designed to enhance both classification accuracy and interpretabilityby leveraging the TabNet architecture with an improved attentive module. Thisdesign ensures robust gradient propagation and computational stability.Additionally, we present a novel evaluation metric, InterpreStability, whichquantifies the stability of a model's interpretability. The proposed model andmetric mark a significant stride forward in explainable models' research,setting a standard for transparency and interpretability in AI model design andapplication across diverse sectors. InterpreTabNet surpasses other leadingsolutions in tabular data analysis across varied application scenarios, pavingthe way for further research into creating deep-learning models that are bothhighly accurate and inherently explainable. The introduction of theInterpreStability metric ensures that the interpretability of future models canbe measured and compared in a consistent and rigorous manner. Collectively,these contributions have the potential to promote the design principles anddevelopment of next-generation interpretable AI models, widening the adoptionof interpretable AI solutions in critical decision-making environments.

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