Transforming Privacy Artifacts into Accessible Reports for Non-Technical Stakeholders

2026-05-20

AI Summary

This study addresses the challenge of privacy communication in human–robot collaboration systems within Industry 5.0, where sensitive data monitoring raises significant privacy concerns that are often obscured by technical complexity, leading to mistrust and resistance among non-technical stakeholders. To bridge this gap, the authors propose a novel conceptual framework that integrates Privacy by Design principles with large language models (LLMs), leveraging LLMs for the first time in the requirements engineering process to automatically generate natural-language privacy reports tailored for non-technical audiences from representative human–robot monitoring scenarios. Evaluation across two industrial use cases demonstrates that the approach substantially enhances the comprehensibility of privacy information and supports informed decision-making, thereby addressing a critical accessibility gap in existing privacy communication mechanisms.

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.

Problem

• privacy communication
• non-technical stakeholders
• human-machine collaboration
• Requirements Engineering
• privacy transparency

Innovation

• Privacy by Design
• Large Language Models
• Requirements Engineering
• Human-Centric Systems
• Privacy Transparency