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@article{201178,
        author = {Sunkenapally Subhash and Ms. B. Pramodhini},
        title = {A Trustworthy Artificial Intelligence Framework for Decision Support Using Continual Learning, Explainability, and Uncertainty Estimation},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {2861-2867},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=201178},
        abstract = {Artificial Intelligence has become one of the key enabling technologies in developing decision support systems for various domains such as banking, health care and finance. Despite its success in predictive modeling, traditional AI systems often lack transparency, fail to quantify uncertainty, and are unable to adapt to dynamic environments. These limitations reduce trust and hinder deployment in high-stakes applications.
This paper proposes a comprehensive Trustworthy Artificial Intelligence framework that integrates explainability, uncertainty estimation, and continual learning within a unified decision support system. The framework leverages a neural network-based prediction model enhanced with Monte Carlo Dropout for uncertainty estimation and SHAP for interpretability.We also introduce a human-in-the-loop mechanism for uncertain predictions, ensuring robustness and safety for decision making. Moreover, continual learning has been implemented with Elastic Weight Consolidation to adapt to the changing data distributions. We have tested the proposed framework with a real-world banking dataset and showed its ability to improve predictive performance, model interpretability, and to achieve an uncertainty aware decision routing leading to decreased human work load with higher decision quality.},
        keywords = {Trustworthy AI, Explainable AI, Uncertainty Estimation, Continual Learning, Human-in-the-Loop, Bank Marketing},
        month = {May},
        }