Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

Abstract Recent years have seen a tremendous growth in Artificial Intelligence (AI)-based methodological development in a broad range of domains. It also provides a comprehensive and in-depth evaluation of the XAI frameworks and their efficacy to serve as a starting point of XAI for applied and theoretical researchers.

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• 1 In this rapidly evolving field, large number of methods are being reported using machine learning (ML) and Deep Learning (DL) models.
• 2 Majority of these models are inherently complex and lacks explanations of the decision making process causing these models to be termed as 'Black-Box'.
• 3 One of the major bottlenecks to adopt such models in mission-critical application domains, such as banking, e-commerce, healthcare, and public services and safety, is the difficulty in interpreting them.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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