为机器学习系统补上安全文档短板，提升整体安全性
Expanding ML-Documentation Standards For Better Security
- 在现有模型卡和数据集说明书基础上增加安全信息章节
- 实证发现多数实践者忽视安全文档，标准执行率低
- 适合关注模型安全、合规与可信AI的研究者与工程师
本文基于对现有文献的广泛调研，分析了当前机器学习安全现状及系统、模型与数据集在研究与实践中文档化水平。结果显示，从业者与组织普遍缺乏对安全问题的关注，文档规范性不足，导致整体文档质量偏低。现有标准在实践中未被常规采纳，且信息安全内容常被忽略。因此亟需改进机器学习安全文档，以弥补当前安全短板。为此，我们提出扩展现有文档标准，在模型卡与数据集说明书基础上加入专门的安全信息部分，并建议将此方法推广至所有机器学习文档中。
原文摘要 · Abstract (English)
This article presents the current state of ML-security and of the documentation of ML-based systems, models and datasets in research and practice based on an extensive review of the existing literature. It shows a generally low awareness of security aspects among ML-practitioners and organizations and an often unstandardized approach to documentation, leading to overall low quality of ML-documentation. Existing standards are not regularly adopted in practice and IT-security aspects are often not included in documentation. Due to these factors, there is a clear need for improved security documentation in ML, as one step towards addressing the existing gaps in ML-security. To achieve this, we propose expanding existing documentation standards for ML-documentation to include a security section with specific security relevant information. Implementing this, a novel expanded method of documenting security requirements in ML-documentation is presented, based on the existing Model Cards and Datasheets for Datasets standards, but with the recommendation to adopt these findings in all ML-documentation.
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