Explainable AI Models for Transparent Decision-Making in Business Analytics Dashboards

Abstract

Artificial intelligence-powered business dashboards are increasingly used to drive strategic and operational decisions across industries. However, traditional black-box machine learning models lack transparency, limiting trust, auditability, and widespread adoption in high-impact business settings. This study proposes an explainable AI-enabled dashboard architecture that integrates model interpretability techniques into enterprise analytics systems to support transparent and accountable decision-making. The methodology includes data preprocessing, model training, post-hoc explainability techniques such as SHAP and LIME, surrogate interpretable models, counterfactual analysis, and user-centric visual explanation components. The resulting system provides both predictive insights and intuitive explanations that highlight feature importance, model behavior, and decision rationale for individual instances and global patterns. Applications in finance, healthcare, marketing, supply chain management, and cybersecurity demonstrate the practical utility of explainable dashboards. Experimental evaluation and design considerations confirm that explainable interfaces enhance user trust, regulatory compliance, ethical governance, and adoption of AI-driven analytics in enterprise environments. This research contributes a scalable and interpretable approach to designing responsible business intelligence systems and establishes a foundation for future work in adaptive, real-time, and personalized AI explanations.