Enhancing Data Governance Through Explainable AI: Bridging Transparency and Automation

Abstract

In an age where decisions are increasingly driven by data, robust data governance has become indispensable for maintaining data quality, ensuring regulatory compliance, and upholding accountability. The rise of AI models, however, has introduced new challenges to transparency and governance, as these models often operate as "black boxes" with complex, opaque decision-making processes. This lack of interpretability can hinder trust and create obstacles in critical areas like compliance, ethical considerations, and decision validation. Explainable AI (XAI) emerges as a crucial solution to these challenges by providing insights into how AI models make decisions, thereby enhancing their transparency and trustworthiness. XAI not only demystifies the inner workings of AI but also aligns AI-driven decisions with established governance principles. By making AI models more interpretable, XAI bridges the gap between automation and the need for transparency in data governance. This article explores how XAI can significantly improve data governance practices by examining various approaches to implementing XAI, evaluating its impact on regulatory compliance, and presenting real-world case studies where XAI has been effectively integrated. Through detailed analysis and case studies, we demonstrate how XAI can be successfully incorporated into existing data governance frameworks to create more reliable, transparent, and automated processes, ultimately fostering greater trust in AI-driven decision-making.