https://doi.org/10.65770/MJFK8593
ABSTRACT
The integration of Artificial Intelligence (AI) in marketing automation has transformed customer engagement, data-driven personalization, and campaign optimization across industries. However, the opaque nature of AI decision-making raises concerns about transparency, trust, and ethical compliance, particularly in sensitive domains such as healthcare and finance. This review explores the design of Explainable AI (XAI)-based marketing automation architectures that prioritize interpretability, fairness, and regulatory alignment. It examines how explainability frameworks—such as SHAP, LIME, and counterfactual reasoning—can enhance model transparency without compromising predictive accuracy. The paper compares architectural strategies for embedding XAI within Customer Relationship Management (CRM), lead scoring, and content personalization systems in healthcare and financial institutions. By analyzing recent advancements in hybrid explainability models, knowledge graphs, and AI auditing pipelines, this review highlights how organizations can achieve responsible automation while meeting sector-specific compliance standards like HIPAA, GDPR, and Basel III. The study concludes by outlining a reference architecture for XAI-driven marketing automation that balances algorithmic interpretability with business performance, supporting ethical personalization, trustful decision-making, and sustainable digital transformation across regulated industries.
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