  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.

Machine Learning;Artificial Intelligence