Metadata Management for AI-Augmented Data Workflows

2025-08-09

AI Summary

In AI-enhanced data workflows, human-AI collaboration, tool heterogeneity, and opaque model decisions impede comprehensive metadata capture, severely hindering data lineage tracing and reproducibility. To address this, we propose TableVault—a novel metadata governance framework that uniquely integrates database-grade reliability guarantees with AI-native design principles. TableVault introduces a declarative operation builder, lineage-aware references, and fine-grained execution-state tracking to enable end-to-end lineage reconstruction and operational context preservation across heterogeneous tools and in partially observable environments. It further establishes a standardized metadata layer that unifies metadata representation across ingestion, transformation, and consumption stages. Evaluated on a document classification task, TableVault fully captures lifecycle metadata of complex AI pipelines, yielding substantial improvements in transparency, auditability, and reproducibility—demonstrating robustness under realistic, fragmented tooling conditions.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

AI-augmented data workflows introduce complex governance challenges, as both human and model-driven processes generate, transform, and consume data artifacts. These workflows blend heterogeneous tools, dynamic execution patterns, and opaque model decisions, making comprehensive metadata capture difficult. In this work, we present TableVault, a metadata governance framework designed for human-AI collaborative data creation. TableVault records ingestion events, traces operation status, links execution parameters to their data origins, and exposes a standardized metadata layer. By combining database-inspired guarantees with AI-oriented design, such as declarative operation builders and lineage-aware references, TableVault supports transparency and reproducibility across mixed human-model pipelines. Through a document classification case study, we demonstrate how TableVault preserves detailed lineage and operational context, enabling robust metadata management, even in partially observable execution environments.

Problem

• Address governance challenges in AI-augmented data workflows
• Capture comprehensive metadata in heterogeneous dynamic workflows
• Ensure transparency and reproducibility in human-AI collaboration

Innovation

• TableVault framework for human-AI metadata governance
• Standardized metadata layer with lineage tracking
• Declarative operation builders for transparency