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Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges

Naeem Ullah, Javed Ali Khan, Ivanoe De Falco, Giovanna Sannino

2024ACM Computing Surveys30 citationsDOIOpen Access PDF

Abstract

There is an urgent need in many application areas for eXplainable ArtificiaI Intelligence (XAI) approaches to boost people’s confidence and trust in Artificial Intelligence methods. Current works concentrate on specific aspects of XAI and avoid a comprehensive perspective. This study undertakes a systematic survey of importance, approaches, methods, and application domains to address this gap and provide a comprehensive understanding of the XAI domain. Applying the Systematic Literature Review approach has resulted in finding and discussing 155 papers, allowing a wide discussion on the strengths, limitations, and challenges of XAI methods and future research directions.

Topics & Concepts

Computer scienceArtificial intelligenceMachine learningExplainable Artificial Intelligence (XAI)Machine Learning in HealthcareAnomaly Detection Techniques and Applications
Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges | Litcius