A guide to Quantus: an explainable AI toolkit

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.

Explainable artificial intelligence (XAI) refers to a set of processes and strategies that enable humans to comprehend and trust the results of machine learning algorithms. “Explainable AI” refers to the ability to define an AI model, its predicted impact, and potential biases.

It contributes to the definition of model correctness, fairness, transparency, and outcomes in AI-powered decision-making. The ability of an organization to generate trust and confidence is critical when deploying AI models. AI explainability also helps to implement a responsible AI development strategy.

Explainable AI is analogous to “showing your work” in a math problem. All AI decision-making and machine learning processes do not take place in a black box – it is a transparent service designed to be dissected and understood by human practitioners. In order to add ‘explanation’ to the output, input/output mapping is essential.

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