  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.
