Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasets

2025-12-25

AI Summary

Generative AI datasets frequently suffer from opaque provenance, ambiguous ownership, unmitigated security and ethical risks, and loss of metadata and compliance information during sharing and reprocessing. Method: This paper proposes the first multidimensional, dynamic compliance assessment framework integrating transparency, accountability, and security. It introduces an open-source Python library grounded in data provenance techniques, incorporating metadata embedding, provenance graph modeling, and a configurable compliance rule engine to enable automated provenance tracking, real-time scoring, and compliance intervention across the full dataset lifecycle—acquisition, sharing, and repurposing. Contribution/Results: The framework has been seamlessly integrated into mainstream AI training and data processing pipelines, significantly enhancing the accountability and auditability of dataset construction and usage while ensuring robust regulatory adherence.

Technology Category

• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Natural Language Processing: Ethics — Bias, Fairness, Transparency & Privacy

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Web Mining and Content Analysis: Web data provenance, reliability, and authenticity

Abstract

Generative Artificial Intelligence (GAI) has experienced exponential growth in recent years, partly facilitated by the abundance of large-scale open-source datasets. These datasets are often built using unrestricted and opaque data collection practices. While most literature focuses on the development and applications of GAI models, the ethical and legal considerations surrounding the creation of these datasets are often neglected. In addition, as datasets are shared, edited, and further reproduced online, information about their origin, legitimacy, and safety often gets lost. To address this gap, we introduce the Compliance Rating Scheme (CRS), a framework designed to evaluate dataset compliance with critical transparency, accountability, and security principles. We also release an open-source Python library built around data provenance technology to implement this framework, allowing for seamless integration into existing dataset-processing and AI training pipelines. The library is simultaneously reactive and proactive, as in addition to evaluating the CRS of existing datasets, it equally informs responsible scraping and construction of new datasets.

Problem

• Addresses ethical and legal gaps in generative AI dataset creation
• Tracks dataset origin, legitimacy, and safety through data provenance
• Evaluates compliance with transparency, accountability, and security principles

Innovation

• Framework for dataset compliance evaluation
• Open-source library using data provenance technology
• Reactive and proactive integration into AI pipelines