Document Type

Article

Publication Date

7-2-2026

Abstract

Artificial Intelligence (AI) models are often criticized for their black-box nature, particularly in high-stakes domains such as finance, healthcare, and business decision-making, where transparency, accountability, and trust are essential. As machine learning advances with more complex architectures, including deep neural networks and ensemble models, the need for explainability becomes increasingly critical. This study focuses on Explainable Artificial Intelligence (XAI) as a key approach to enhance interpretability in classification, regression, and clustering tasks that serve as the foundation of data-driven analytical systems. XAI methods such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and Integrated Gradients provide a means to unravel model behavior by identifying influential features and explaining their contribution toward predictions. This paper provides a comprehensive review of XAI techniques applied across various domains, highlighting their ability to enhance understanding, validate model outcomes, and foster user trust. Additionally, the study emphasizes the growing importance of XAI in business-oriented applications such as customer segmentation, product recommendation, and sales forecasting, where interpretability is directly linked to strategic decision quality. By ensuring explanations accompany predictions, organizations can achieve more ethical and informed decision-making that improves stakeholder confidence and operational performance. The findings of this research indicate that integrating XAI within machine learning pipelines is not only beneficial but necessary for advancing responsible and interpretable AI solutions that support sustainable economic growth and improved real-world outcomes.

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