Explainable artificial intelligence: principles, methods and applications

Sereda-Kalinin P.Y., Vlasova A.S.
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Explainable Artificial Intelligence (XAI) is a field of artificial intelligence aimed at creating methods and tools for generating interpretable and human-understandable explanations of AI decisions. The relevance of model explainability increases with the deployment of artificial intelligence in critical domains (healthcare, finance, law), where algorithmic opacity can lead to serious consequences for users and society. This work presents an analytical review of the current state of the XAI field, covering theoretical foundations, methodology, and practical applications.

The examined explainable AI methods were selected and systematized based on a multi-level classification of XAI methods by problem formulation (goal, target audience, data type), methodology (application stage, model-specificity, methods, scale), and result form (representation, presentation, evaluation metrics).

A comparative analysis of explainable AI methods for various application domains is conducted. For classical machine learning, SHAP and LIME are examined in detail, revealing their theoretical foundations, computational characteristics, and limitations. For computer vision, gradient-based methods (SmoothGrad, Integrated Gradients), activation visualization methods (Grad-CAM, Grad-CAM++), perturbation-based methods (RISE, Occlusion), and conceptual explanations (TCAV, Network Dissection) are systematized. Special attention is paid to the specifics of applying XAI to natural language processing and large language models, including analysis of the faithfulness of Chain-of-Thought reasoning, natural language explanations, and attribution graph methods. Fundamental limitations of existing approaches to LLM explainability are identified and directions for future research are defined.

The review results demonstrate that XAI methods have reached significant maturity in classical machine learning and computer vision, however, their application to large language models remains an open research problem requiring the development of new explanation paradigms.

Keywords: explainable artificial intelligence, XAI, interpretability, model transparency, machine learning, deep learning, large language models
Citation in English: Sereda-Kalinin P.Y., Vlasova A.S. Explainable artificial intelligence: principles, methods and applications // Computer Research and Modeling, 2026, vol. 18, no. 2, pp. 211-241
Citation in English: Sereda-Kalinin P.Y., Vlasova A.S. Explainable artificial intelligence: principles, methods and applications // Computer Research and Modeling, 2026, vol. 18, no. 2, pp. 211-241
DOI: 10.20537/2076-7633-2026-18-2-211-241
URL: http://crm-en.ics.org.ru/journal/article/3707/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

Copyright © 2026 Sereda-Kalinin P.Y., Vlasova A.S.

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