NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble Techniques
This work addresses the need for more transparent and interpretable deep learning models, offering tools and insights for researchers and practitioners in AI, though it appears incremental as it builds on existing XAI ensemble techniques.
The paper tackled the problem of comparing and improving explainable AI (XAI) ensemble methods by introducing NormEnsembleXAI, a novel method using normalization and functions like minimum, maximum, and average to enhance interpretability, and it provided a library to facilitate practical implementation.
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.