Tracing the Truth: AI-Driven Data Lineage for FATE Integrity

As data-driven decision-making becomes integral to various industries, ensuring data integrity has never been more critical. With the growing reliance on data across sectors, the need for transparent and auditable data pipelines is essential. This paper explores the concept of AI-driven data lineage within the context of Federated Learning, Artificial Intelligence, and Trusted Execution (FATE) integrity. We propose a robust framework that leverages AI technologies to trace, verify, and maintain data integrity through the entire lifecycle, ensuring trustworthiness in machine learning systems. This framework addresses challenges of ensuring data provenance, authenticity, and transparency in decentralized data environments. We present the design, implementation, and potential impacts of this approach, focusing on its application to Federated Learning and its synergy with FATE principles.