Explainable AI Governance For policy Control Mapping in Self Service Business Intelligence Platforms

Abstract

Self-Service Business Intelligence (SSBI) platforms have rapidly enabled business users to access, analyze and visualize data without the support of IT or Analytics team, making using these tools a game-changer for any organization and its decision-making process. But as more users gain control over the process, governance and compliance of policies, transparency, accountability and secure use of data are significant challenges. This paper proposes a framework for Explainable Artificial Intelligence (XAI) Governance for policy-controlled mapping in SSBI Environments. The methodology follows the Activity-based policy mapping, Compliance confidence evaluation, Governance risk assessment and Explainability-driven decision analysis of user actions with organizational policies. An intelligent governance layer continuously analyzes activities, makes governance decision sand explains the decisions in a comprehensible way, which will be available for policy enforcement reasons. Experimental evaluation demonstrates that whereas existing approaches attest to poorer performance in terms of good governance. Experimental evaluation shows that good governance is also improved as compared to the existing practices. The proposed solution has a Policy Mapping Efficiency of 96.5%, Compliance Assurance Rate of 97.4%, Explainability Index of 95.8% and Governance Trust Score of 96.2%, with a Risk Reduction Rate of 94.7%. It is observed that Explainable Governance Analytics data shows improvement of 4.8%, 4.6%, 6.5%, 6.1% and 5.2% for each of the following, respectively. The outcome shows that the framework works well to support good governance of modern SSBI platforms that is transparent, trusted and policy-compliant.