Explainable AI Models for Real-Time Decision Support Systems
DOI:
https://doi.org/10.15662/IJEETR.2026.0801016Keywords:
Explainable AI (XAI), Decision Support Systems, Real-Time Analytics, Interpretable Machine Learning, Model Transparency, Human–AI Interaction, Predictive Modeling, Trustworthy AIAbstract
Explainable Artificial Intelligence (XAI) has emerged as a critical component in modern real-time decision support systems, where transparency, trust, and accountability are essential. Traditional AI models, particularly deep learning techniques, often function as “black boxes,” making it difficult for users to understand how decisions are derived. This research paper explores the design, implementation, and evaluation of explainable AI models tailored for real-time decision support environments. The study focuses on integrating interpretable machine learning techniques, such as feature importance analysis, rule-based explanations, and model-agnostic explanation methods, into time-sensitive applications. It also examines the trade-off between model accuracy and interpretability in dynamic decision-making scenarios such as healthcare monitoring, financial risk assessment, and smart industrial systems. Furthermore, the paper proposes a hybrid explainable framework that combines high-performance predictive models with post-hoc explanation tools to ensure both efficiency and transparency. Experimental analysis demonstrates that the proposed approach enhances user trust, improves decision quality, and enables faster human–AI collaboration without significantly compromising system performance. The findings highlight the importance of explainability as a foundational requirement for deploying AI-driven real-time decision support systems in critical domains.
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