EXPLAINABLE ARTIFICIAL INTELLIGENCE FOR RELIABLE DECISION SUPPORT: TECHNIQUES, APPLICATIONS, AND FUTURE RESEARCH CHALLENGES

Artificial intelligence (AI) has become an important component of modern computing systems, supporting applications in healthcare, finance, education, cybersecurity, manufacturing, and business management. Although machine-learning models can identify complex patterns and generate predictions, many advanced models operate in ways that are difficult for users to understand. This lack of transparency can create challenges when decisions affect individuals, organizations, or critical operational processes. Explainable artificial intelligence (XAI) addresses this problem by developing methods that make model predictions and decision processes more understandable to human users. This paper presents a conceptual review of explainable AI, focusing on its fundamental principles, major techniques, practical applications, implementation challenges, and future research opportunities. It discusses feature attribution, interpretable models, counterfactual explanations, and model-agnostic approaches. The paper also examines the role of explainability in decision support, model evaluation, fairness assessment, and responsible AI deployment. Particular attention is given to the limitations of explanation methods, including instability, misleading interpretations, computational costs, and the potential mismatch between explanations and actual model behaviour. The discussion indicates that explainability can improve the interpretation and evaluation of AI systems when supported by appropriate validation and human oversight. However, explanations alone cannot guarantee fairness, accuracy, or trustworthiness. Future research should focus on evaluating explanation quality, improving robustness, developing user-centred explanation methods, and integrating explainability into the complete machine-learning lifecycle.