NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble
  Techniques
Weronika Hryniewska-Guzik Bartosz Sawicki Przemysław Biecek
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.