Transforming Privacy Artifacts into Accessible Reports for Non-Technical Stakeholders

TL;DR

This paper presents a framework that uses Large Language Models to convert technical privacy artifacts into accessible reports, aiding non-technical stakeholders in understanding privacy implications in Industry 5.0 systems.

Contribution

It introduces a novel conceptual framework leveraging LLMs to make privacy threats and mitigations understandable for non-technical stakeholders in industrial systems.

Findings

Initial insights from two industry use cases demonstrate the framework's applicability.

Evaluation shows the generated reports are understandable and informative for non-technical stakeholders.

The approach supports early stakeholder involvement and transparency in privacy management.

Abstract

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 RE@Next 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.