This paper presents a comprehensive comparative analysis of explainable
artificial intelligence (XAI) ensembling methods. Our research brings three
significant contributions. Firstly, we introduce a novel ensembling method,
NormEnsembleXAI, that leverages minimum, maximum, and average functions in
conjunction with normalization techniques to enhance interpretability.
Secondly, we offer insights into the strengths and weaknesses of XAI ensemble
methods. Lastly, we provide a library, facilitating the practical
implementation of XAI ensembling, thus promoting the adoption of transparent
and interpretable deep learning models.

本文提供了一项对可解释人工智能（XAI）集成方法的综合比较分析。我们的研究做出了三个重要贡献：首先，我们引入了一种新的集成方法 NormEnsembleXAI，通过与正规化技术结合利用最小值、最大值和平均值函数来增强可解释性；其次，我们深入探讨了 XAI 集成方法的优点和局限性；最后，我们提供了一个库，方便实现 XAI 集成，从而促进透明和可解释的深度学习模型的采用。