Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence

By Sajid Ali, T. Abuhmed, Shaker El-Sappagh, Khan Muhammad, J. Alonso-Moral, Roberto Confalonieri, Riccardo Guidotti, Javier Del Ser, N. Díaz-Rodríguez, Francisco Herrera. Information Fusion, 2023

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How researchers see this paper

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A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME (2023, background): “XAI is indispensable to increase the model transparency and the trust of end-users in the model outcome [6] [7].”
Connecting the Dots in Trustworthy Artificial Intelligence: From AI Principles, Ethics, and Key Requirements to Responsible AI Systems and Regulation (2023, background): “Existing literature and contributions already made in this field include broad insights into what is yet to be achieved [54, 57, 58].”
Low-cost concept-based localized explanations: How far can we get with training-free approaches? (2026, unknown): “However, multimodal models typically rely on paired image–text training, while current eXplainable Artificial Intelligence (XAI) [2], [3] methods remain limited in delivering low-level, fine-grained explanations for such systems.”