Explainable Artificial Intelligence has gained significant attention due to
the widespread use of complex deep learning models in high-stake domains such
as medicine, finance, and autonomous cars. However, different explanations
often present different aspects of the model's behavior. In this research
manuscript, we explore the potential of ensembling explanations generated by
deep classification models using convolutional model. Through experimentation
and analysis, we aim to investigate the implications of combining explanations
to uncover a more coherent and reliable patterns of the model's behavior,
leading to the possibility of evaluating the representation learned by the
model. With our method, we can uncover problems of under-representation of
images in a certain class. Moreover, we discuss other side benefits like
features' reduction by replacing the original image with its explanations
resulting in the removal of some sensitive information. Through the use of
carefully selected evaluation metrics from the Quantus library, we demonstrated
the method's superior performance in terms of Localisation and Faithfulness,
compared to individual explanations.

通过合并深度分类模型生成的解释，研究人员探索了可解释人工智能的潜力，以揭示模型行为的更一致和可靠的模式，进而评估模型所学到的表示。使用选定的 Quantus 库评估指标，证明该方法在本地化和可信度方面的性能优于单个解释。