用于半自动化检查研究产出的多语言工具包
A multi-language toolkit for the semi-automated checking of research outputs
AI总结：
本文介绍开源多语言工具包SACRO，在安全数据环境中半自动化检查研究产出的隐私披露，通过轻量级Python包辅助人工检查员应用统计披露控制技术，提升审核效率与可审计性。
摘要：
本文介绍了一个免费开源工具包，支持在安全数据环境中对研究产出（SACRO）进行隐私披露的半自动化检查。SACRO是一个框架，在研究人员进行分析时，即时应用基于最佳实践原则的统计披露控制（SDC）技术。SACRO旨在辅助人工检查员，而非像当前基于自动化规则的方法那样试图取代他们。该工具包由一个轻量级Python包组成，置于生成表格、图表和统计模型等产出的知名分析工具之上。该包增加了以下功能：(i)根据一系列常用的披露测试自动识别潜在涉密产出；(ii)根据要求应用可选的披露缓解策略；(iii)报告应用SDC的原因；(iv)生成可信研究环境工作人员可用于简化工作流程并维护可审计记录的简单摘要文档。这创造了动态上的明确转变，使SDC成为与研究人员共同完成的工作，而非强加于他们的工作，并实现了与检查员更高效的沟通。图形用户界面通过以即时可访问的格式显示请求的产出和检查结果，突出显示已识别的问题、潜在的缓解选项以及追踪所做的决策，从而为人工检查员提供支持。通过提供与核心Python后端接口的前端包，支持研究人员使用的主要分析编程语言（Python、R和Stata）。源代码、包和文档在MIT许可证下于https://github.com/AI-SDC/ACRO提供。
英文摘要：
This article presents a free and open source toolkit that supports the semi-automated checking of research outputs (SACRO) for privacy disclosure within secure data environments. SACRO is a framework that applies best-practice principles-based statistical disclosure control (SDC) techniques on-the-fly as researchers conduct their analyses. SACRO is designed to assist human checkers rather than seeking to replace them as with current automated rules-based approaches. The toolkit is composed of a lightweight Python package that sits over well-known analysis tools that produce outputs such as tables, plots, and statistical models. This package adds functionality to (i) automatically identify potentially disclosive outputs against a range of commonly used disclosure tests; (ii) apply optional disclosure mitigation strategies as requested; (iii) report reasons for applying SDC; and (iv) produce simple summary documents trusted research environment staff can use to streamline their workflow and maintain auditable records. This creates an explicit change in the dynamics so that SDC is something done with researchers rather than to them, and enables more efficient communication with checkers. A graphical user interface supports human checkers by displaying the requested output and results of the checks in an immediately accessible format, highlighting identified issues, potential mitigation options, and tracking decisions made. The major analytical programming languages used by researchers (Python, R, and Stata) are supported by providing front-end packages that interface with the core Python back-end. Source code, packages, and documentation are available under MIT license at https://github.com/AI-SDC/ACRO
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。
