Explainable AI (XAI) Governance in CCaaS Platforms: A Trust, Compliance, and Accountability Model for Enterprise Contact Centers

Abstract

The proliferation of artificial intelligence (AI) in Contact Center as a Service (CCaaS) platforms has introduced significant operational efficiencies but raised critical concerns regarding transparency, compliance, and stakeholder trust. This paper presents a comprehensive Explainable AI (XAI) Gover- nance Framework specifically designed for enterprise contact centers, addressing the imperatives of trust, regulatory compli- ance, and algorithmic accountability. We propose a three-pillar model encompassing technical explainability mechanisms, regu- latory compliance protocols, and stakeholder trust frameworks. The methodology integrates SHAP (SHapley Additive exPlana- tions) and LIME (Local Interpretable Model-agnostic Explana- tions) for model interpretability, continuous bias monitoring sys- tems, and real-time audit trail generation. Empirical validation across three multinational organizations demonstrates substantial improvements: 67% enhancement in compliance audit readiness, 81% reduction in regulatory findings, and 46-90% improvement in stakeholder trust metrics. The framework addresses GDPR, CCPA, and EU AI Act requirements while maintaining system performance with explanation latencies under 2 seconds. This research contributes a practical, scalable governance model for responsible AI deployment in customer-facing enterprise systems.