The transition toward Industry 5.0 is reshaping industrial work environments with an emphasis on human-centricity, enabling close collaboration between humans and machines to enhance productivity and flexibility. However, such systems typically require monitoring of human workers and operators, often involving sensitive data, raising significant privacy concerns. As a result, affected workers and unions frequently reject human-machine collaboration features due to a lack of transparency regarding privacy threats and implemented mitigation strategies. To enable early stakeholder involvement, establish trust, and support informed decision-making, privacy implications must be communicated in a way understandable to non-technical stakeholders. Yet, current Requirements Engineering (RE) practices provide limited methodological support for making privacy threats and mitigations accessible to non-technical stakeholders (e.g., individual workers or their representative unions). In this paper, we propose a conceptual framework that guides software design from human monitoring-related use cases and requirements to informed decision-making guidance focusing on non-technical stakeholders. Building on principles such as Privacy by Design, the framework leverages Large Language Models (LLMs) to transform technical artifacts into accessible privacy reports. We share initial insights from two industry use cases, evaluate the quality of the generated reports, and outline future research directions toward integrating privacy transparency into RE processes for human-centric industrial systems.

翻译：向工业5.0的转型正在重塑工业工作环境，强调以人为中心，促进人与机器之间的紧密协作，以提高生产力和灵活性。然而，此类系统通常需要监控人类工人和操作员，往往涉及敏感数据，从而引发重大的隐私问题。因此，受影响的工人和工会经常拒绝人机协作功能，因为缺乏关于隐私威胁及已实施缓解策略的透明度。为了促进利益相关方的早期参与、建立信任并支持知情决策，必须以非技术利益相关方能够理解的方式传达隐私影响。然而，当前的软件需求工程实践在使隐私威胁及缓解措施对非技术利益相关方（例如，个体工人或其代表工会）可理解方面，提供的方法论支持十分有限。在本文中，我们提出了一个概念框架，该框架指导从与人类监控相关的用例和需求出发的软件设计，最终形成面向非技术利益相关方的知情决策指导。该框架以“隐私设计”等原则为基础，利用大语言模型将技术工件转化为易于理解的隐私报告。我们分享了来自两个行业用例的初步见解，评估了生成报告的质量，并概述了将隐私透明性整合到以人为中心的工业系统软件需求工程流程中的未来研究方向。