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Explainable AI refers to strategies and procedures used in the application of artificial intelligence (AI) that allow human specialists to understand the solution's findings. Explainable AI refers to strategies and procedures used in the application of artificial intelligence (AI) that allow human specialists to understand the solution's findings. To ensure that explanation methods are correct, they must be systematically reviewed and compared. In contrast to achieving quantitative explanation, in this article, we will discuss Quantus, a Python library that evaluates a convolutional neural network's working, predictions and explanation of parameters. Below is the list of major points that will be discussed in this article.