Результаты поиска по 'XAI':
Найдено статей: 4
  1. Editor’s note
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1533-1538
  2. Editor’s note
    Computer Research and Modeling, 2026, v. 18, no. 2, pp. 205-208
  3. Sereda-Kalinin P.Y., Vlasova A.S.
    Explainable artificial intelligence: principles, methods and applications
    Computer Research and Modeling, 2026, v. 18, no. 2, pp. 211-241

    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.
  4. Qaisrani S.N., Khattak A., Zubair Asghar M., Kuleev R., Imbugva G.
    Efficient diagnosis of cardiovascular disease using composite deep learning and explainable AI technique
    Computer Research and Modeling, 2024, v. 16, no. 7, pp. 1651-1666

    During the last several decades, cardiovascular disease has surpassed all others as the leading cause of mortality in both high-income and low-income countries. The mortality rate from heart disorders may be lowered with early identification and close clinical monitoring. However, it is not feasible to adequately monitor patients every day, and 24-hour consultation with a doctor is not a feasible option, since it requires more sagacity, time, and knowledge than is currently available.

    In this study, we examine the Explainable Artificial Intelligence (XAI) technique, namely, the SHAP interpretability approach, in order to educate the medical professionals about the Explainable AI (XAI) methods that can be helpful in healthcare. The XAI methods enhance the trust and understandability of both practitioners and Health Researchers in AI Models. In this work, we propose a composite Deep Learning model: Bi-LSTM+CNN model to effectively predict heart disease from patient data. After balancing the dataset, the Bi-LSTM+CNN model was used. In contrast to other studies, our proposed hybrid deep learning model produced excellent experimental results, including 99.05% accuracy, 99% precision, 99% recall, and 99% F1-score.

    Keywords: explainable AI, cross-validation, backward elimination, REFCV, cardiovascular disease, healthcare.

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