As of my last knowledge update in January 2022, several methods and approaches have been developed to address the challenges of explainability in machine learning. Keep in mind that the field is rapidly evolving, and there might be additional developments beyond that time.
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It’s recommended to refer to the latest conference proceedings, journals, and research repositories for the most up-to-date information on recent methods in explainable artificial intelligence and machine learning.1
References
1Kaushik R. Explainability in Machine Learning: Bridging the Gap Between Model Complexity and Interpretability. Edu Journal of International Affairs and Research. 2023;2(4):57-63. Accessed March 19, 2024. https://edupublications.com/index.php/ejiar/article/view/38