人工智能供应链中的监管合规准备：审视Hugging Face中的数据集
Regulatory compliance-readiness in the AI Supply Chain: examining datasets in Hugging Face
AI总结：
研究人工智能供应链中Hugging Face数据集的监管合规性，以数据隐私为重点，通过数据集卡片和结构概述，对11682个数据集进行自动和人工分析，揭示问题，凸显相关审查和工具研究的必要性。
摘要：
通过Hugging Face可获取大量数据集用于训练众多人工智能模型。当这些数据集不符合数据隐私规范或未披露数据来源等时，人工智能供应链会出现监管问题。本文以数据隐私为重点，在Hugging Face数据集框架下审视监管合规准备，对数据集进行分析，结果显示需更详细审查及相关工具研究。
英文摘要：
A very large number of datasets are made available via Hugging Face (HF). These datasets are used to train a vast number of AI models, also available via HF. There are regulatory issues that might arise in the AI supply chain when those datasets do not follow, for instance, data privacy practices, or do not disclose their data sources, cleaning and preparation processes. In this preliminary work, regulatory compliance-readiness is examined as a quality attribute in the framework of HF datasets, with a main focus on data privacy (e.g. GDPR, CCPA). Towards this direction, an analysis on regulatory compliance (e.g. with privacy laws) of the dataset using the dataset card and the dataset structure overview as starting points has been performed. We collected 11,682 HF datasets that have a dataset card, are not gated and have at least 500 downloads and analyzed the datasets using automated techniques and manual analysis on a sample of the dataset. The results show that a very small number of datasets are explicit on the datasets creation processes and mention data sources, while some make mentions to Personally Identifiable Information (PII) or other sensitive data in their data schemas. These results show the need for more detailed examinations of regulatory compliance in AI datasets and models, and research on tools that provide guidance to practitioners and automate the compliance process.
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。
