This review provides a comprehensive, structured and formal analysis of the current state of XAI along four dimensions, including explanation types, methods, applications and evaluation metrics, aimed at providing a concise and informative review to researchers and practitioners in the field of XAI.
It is concluded that explainability is a necessary, though not sufficient, condition for trustworthy Al, and concrete directions for future research are outlined, including standardised benchmarks, human-centred evaluation, and explainability for large generative models.
Afna Ashraff M, Archana K, Buthaina Buthaina et al.· International Journal of Tec...· 0 citations
Experimental findings show the effectiveness of XAI techniques to enhance interpretability without causing a major loss in predictive accuracy and the practical implications, limitations, and research directions on the future of explainable and trustworthy AI systems.
Unknown authors· International Journal of App...· 0 citations
This review provides a comprehensive overview of recent developments in XAI for healthcare, focusing on key methods for achieving interpretability, such as intrinsically interpretable models, and post-hoc explanation methods such as SHAP, LIME, Grad-CAM, attention mechanisms, surrogate models, and counterfactual explan...
S. R. Ali, .a. Meher Nisha, K. A. Sathik et al.· International Journal of Int...· 0 citations
A structured narrative review using predefined searches of Web of Science Core Collection and Scopus to synthesize empirical XAI applications across six water-research domains, with emphasis on method selection, model and data compatibility, explanation reliability, and operational implementation.
A methodology is advanced to embed explainability in the AI decision-making process, starting from data preprocessing to generating explanations and human evaluation, and the results highlight the potential of explainability to enhance human comprehension and foster responsible use of AI systems in high-stakes decision...
Mahabala H. N.· International Journal of Mod...· 0 citations
A thorough and stepwise analysis of how XAI was created in current decision systems indicates that explainable models, in addition to increasing interpretability, can also help to improve debugging, bias detection and ethical AI deployment.
Meena Krishnan· International Journal of Mod...· 0 citations
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