Data governance in artificial intelligence (AI) is a critical framework that ensures the responsible management of data throughout its lifecycle, emphasizing ethical considerations and human-centered approaches. This abstract outlines the key aspects of data governance as it pertains to AI, highlighting its foundational concepts, ethical implications, and practical applications. The introduction establishes the significance of data governance in AI, focusing on human-centered methodologies that prioritize individual rights and societal values. The foundational concepts section defines data governance principles, detailing the roles of people, processes, and technology in effective AI systems. Ethical considerations are paramount, addressing privacy, data protection, and the balance between data utility and ethical standards. Subsequent sections explore data collection, quality management, storage security, and governance frameworks. They emphasize the importance of bias detection and mitigation strategies to ensure fairness in AI outputs. The document also discusses challenges in multiparty data sharing and compliance with global data protection frameworks such as GDPR, DPDPA, and PIPL. Finally, the conclusion synthesizes the key takeaways, underscoring the necessity of human-centered approaches in shaping future data governance models. By fostering collaboration among stakeholders and adhering to ethical standards, organizations can enhance trust and accountability in AI-driven systems.