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.

翻译：本文对可解释人工智能（XAI）集成方法进行了全面的比较分析。我们的研究做出三项重要贡献。首先，我们提出了一种新的集成方法NormEnsembleXAI，该方法结合最小值、最大值和平均值函数与归一化技术以增强可解释性。其次，我们揭示了XAI集成方法的优势与局限。最后，我们提供了一个库，用于促进XAI集成的实际应用，从而推动透明且可解释的深度学习模型的采用。