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

Ali, Sajid; Abuhmed, Tamer; El–Sappagh, Shaker; Muhammad, Khan; Alonso, José M.; Confalonieri, Roberto; Guidotti, Riccardo; Ser, Javier Del; Díaz-Rodríguez, Natalia; Herrera, Francisco

doi:10.1016/j.inffus.2023.101805

articleInformation FusionApr 18, 2023HYBRID OA

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

SASajid Ali TATamer Abuhmed SEShaker El–Sappagh KMKhan Muhammad JMJosé M. Alonso

Sungkyunkwan University · Suez University · +10 more institutions

Indexed incrossref

Abstract

Artificial intelligence (AI) is currently being utilized in a wide range of sophisticated applications, but the outcomes of many AI models are challenging to comprehend and trust due to their black-box nature. Usually, it is essential to understand the reasoning behind an AI model’s decision-making. Thus, the need for eXplainable AI (XAI) methods for improving trust in AI models has arisen. XAI has become a popular research subject within the AI field in recent years. Existing survey papers have tackled the concepts of XAI, its general terms, and post-hoc explainability methods but there have not been any reviews that have looked at the assessment methods, available tools, XAI datasets, and other related…

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1,441

total citations

FWCI: 238.05
Percentile: 100%
References: 529

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Authors

10

Topics & keywords

Topics

Keywords

Trustworthiness
Computer science
Artificial intelligence
Computer security

UN Sustainable Development Goals

Peace, Justice and strong institutions

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