Current Volume 8
Explainable AI for Business Intelligence: Enhancing Transparency in Enterprise AI Solutions
Abstract
Artificial Intelligence (AI) integration with Business Intelligence (BI) systems through revolutionary innovations delivers data-oriented insights and operational efficiency improvements to enterprise decisions. The extensive utilization of AI by businesses creates challenges regarding transparency along with accountability along with trust because many working AI models remain non-interpretable to humans. XAI serves as a solution that creates interpretation methodologies to audit and understand AI-derived decisions thus both supports regulatory compliance and builds trust between AI stakeholders. The essential role of XAI in improving transparency within enterprise AI solutions receives detailed analysis in this document through mention of SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) along with decision trees and various rule-based methods. This research investigates both ethical and regulatory aspects of AI transparency as well as evaluates the interpretation model performance trade-offs and demonstrates how XAI helps achieve fairness improvements in AI-driven BI applications. The research executes comparative investigations with case-based examples to deliver an organized approach for firms to execute XAI deployment at optimized performance levels. Businesses should implement explainable AI systems to their business intelligence frameworks because these techniques improve both decision-making precision and user trust and regulatory adherence while providing competitive market benefits. The paper finishes with current XAI trends evaluations and recommendations regarding enterprise efforts to establish transparent AI BI solutions.
Keywords
Explainable AI (XAI), Business Intelligence (BI), Enterprise AI Solutions, AI Transparency and Accountability, Interpretable Machine Learning
Citations
IRE Journals:
Ashitosh Chitnis , Shishir Tewari
"Explainable AI for Business Intelligence: Enhancing Transparency in Enterprise AI Solutions" Iconic Research And Engineering Journals Volume 7 Issue 9 2024 Page 453-467
IEEE:
Ashitosh Chitnis , Shishir Tewari
"Explainable AI for Business Intelligence: Enhancing Transparency in Enterprise AI Solutions" Iconic Research And Engineering Journals, 7(9)
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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