Data governance for AI tools faces several complex challenges. Ensuring data quality and mitigating bias is paramount, as flawed or prejudiced datasets directly lead to unfair or inaccurate AI outcomes. Another significant hurdle is maintaining data privacy and complying with evolving regulations like GDPR or CCPA, especially when processing sensitive personal information across diverse data sources. Furthermore, the lack of explainability and interpretability in many advanced AI models complicates governance, making it difficult to understand decision-making processes and ensure accountability. Establishing clear data lineage and traceability is also crucial for auditing and debugging AI systems effectively. These challenges necessitate robust frameworks that address data ethics, security, and lifecycle management throughout the AI development pipeline. More details: https://bacsychuyenkhoa.net/301.php?url=https://4mama.com.ua/