Machine Learning Approaches for Intelligent Data Governance

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This paper examines machine-learning approaches for intelligent data governance through an integrative conceptual review of data governance, data quality, data-centric artificial intelligence, explainable AI, privacy, responsible AI, and machine-learning operations. It proposes an Intelligent Machine-Learning Data Governance Framework (IMLDGF) integrating data ingestion, automated discovery, classification, quality intelligence, privacy and risk analytics, policy recommendation, explainability, human oversight, and continuous feedback. The study argues that machine learning should not replace data stewards or governance authorities; rather, it should augment organizational decision-making by converting governance from a reactive and periodic activity into a predictive, adaptive, and continuously monitored process. Particular attention is given to supervised learning, unsupervised learning, anomaly detection, natural-language processing, clustering, ensemble methods, graph learning, and explainable AI. The paper further identifies challenges concerning algorithmic bias, poor training data, privacy, concept drift, false positives, explainability, accountability, security, regulatory compliance, and excessive automation. It concludes that intelligent data governance requires a human-centred architecture in which machine intelligence increases scalability and responsiveness while governance policies, ethical principles, organizational responsibilities, and human judgment retain ultimate authority.