标题： 扩展机器学习文档标准以提高安全性 显示英文标题

标题： Expanding ML-Documentation Standards For Better Security

摘要： 本文基于对现有文献的广泛综述，介绍了当前机器学习安全性和机器学习系统、模型和数据集在研究和实践中的文档状况。 它表明机器学习实践者和组织普遍缺乏对安全方面的认识，并且在文档方面往往采用非标准化的方法，导致机器学习文档的整体质量较低。 现有的标准在实践中并未被定期采用，IT安全方面的问题也常常未包含在文档中。 由于这些因素，ML中需要改进安全文档，作为解决现有ML安全差距的一个步骤。 为了实现这一点，我们建议扩展现有的ML文档标准，以包括一个包含具体安全相关信息的安全部分。 实施这一点，提出了一种新的扩展方法来记录ML文档中的安全需求，该方法基于现有的Model Cards和Datasheets for Datasets标准，但建议在所有ML文档中采用这些成果。 显示英文摘要

摘要： 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.