合规评分方案：一种面向生成式人工智能数据集的数据溯源框架
Compliance Rating Scheme: A Data Provenance Framework for Generative AI Datasets
AI总结：
本文提出合规评分方案，用于评估生成式人工智能数据集的透明度、问责制和安全性，同时提供开源库以实现数据溯源，促进负责任的数据集构建与管理。
摘要：
生成式人工智能（GAI）近年来经历了指数级的增长，部分得益于大规模开源数据集的丰富。这些数据集往往使用不受限制且不透明的数据收集实践构建。尽管大多数文献关注GAI模型的开发和应用，但围绕这些数据集创建的伦理和法律问题常被忽视。此外，随着数据集被分享、编辑并进一步在线再生产，其起源、合法性及安全性信息往往丢失。为填补这一空白，我们引入了合规评分方案（CRS），一种用于评估数据集是否符合关键透明度、问责制和安全原则的框架。我们还发布了一个围绕数据溯源技术构建的开源Python库，以实现该框架，允许无缝集成到现有数据集处理和AI训练流程中。该库同时具有反应性和前瞻性，除了评估现有数据集的CRS外，还同样指导负责任的抓取和构建新数据集。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
