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.

翻译：随着人工智能（AI）在各行业的深度融入，对高性能模型的追求日益增强。尽管在提升模型能力及其跨领域适用性方面已取得显著进展，但一个严峻挑战依然存在：许多最先进的模型仍处于"黑箱"状态。这种不透明性不仅导致终端用户难以理解模型决策过程，更阻碍了模型设计者对中间过程的洞察。为解决这些问题，我们提出InterpreTabNet——该模型通过改进注意力模块强化TabNet架构，在提升分类准确率的同时增强可解释性，其设计确保了稳健的梯度传播与计算稳定性。此外，我们创新性地提出InterpreStability评估指标，用于量化模型可解释性的稳定性。所提出的模型与指标标志着可解释模型研究的重大突破，为跨领域AI模型设计与应用中的透明度与可解释性树立了新标杆。在多种应用场景的表格数据分析中，InterpreTabNet均超越现有领先方案，为创建兼具高精度与内在可解释性的深度学习模型开辟了新路径。InterpreStability指标的引入确保了未来模型的可解释性能可被一致、严格地量化比较。这些贡献将共同推动下一代可解释AI模型的设计原则与发展，扩大可解释AI解决方案在关键决策场景中的采纳范围。