Human workers as physical AI data generators: A socio-technical governance model for sustainable manufacturing transformation
This academic paper develops the Socio-Technical Physical AI Data Governance Framework, re-framing manual workers in labour-intensive manufacturing as intentional data generators rather than passive subjects of surveillance. It integrates productivity metrics, ergonomic risk assessment (such as RULA and REBA), and psychological-state monitoring within a single decision engine combining Fuzzy BWM, Bayesian networks, and Fuzzy FMEA. The review-based framework aims to protect worker welfare while sustaining high-quality dataset production for physical AI applications across diverse enterprise scales.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, industrial engineers, and policymakers seeking a structured socio-technical framework to govern physical AI data collection from human workers.
What the document contains
The document covers the intersection of physical artificial intelligence, worker monitoring, ergonomic assessment (RULA, REBA, OWAS), psychological impacts of electronic performance monitoring, and multi-criteria decision-making frameworks for manufacturing data governance.
The framework remains conceptual and lacks empirical field validation, requiring computational implementation and testing against real manufacturing decision scenarios.
- Manual workers in labour-intensive manufacturing are re-conceptualized as intentional Physical AI data generators whose tacit knowledge and physical states form foundational data assets.
- Prevailing surveillance-oriented monitoring elevates worker strain and degrades the quality of motion data through stress-induced adaptation.
- The proposed Socio-Technical Physical AI Data Governance Framework integrates productivity, digital ergonomics, and psychological monitoring into a unified architecture.
- Combining Fuzzy BWM, Bayesian networks, and Fuzzy FMEA provides a decision engine that converts multi-dimensional risks into prioritized governance actions.
- SMEs face acute resource and infrastructure barriers, requiring scalable, open-source governance templates and cooperative models.
· 1. Introduction · 2. Theoretical foundations · 3. Material and methods · 4. Results and discussion: systematic analysis of the literature · 5. Research gap analysis · 6. The socio-technical Physical AI data governance framework · 7. Discussion · 8. Conclusion
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Fairness
3/5Non-discrimination and equitable treatment
demographic-diversity requirements to limit algorithmic bias
7.3.2. Industry standards
screening score · 2026-10-23
Human Oversight
3/5Humans remain in control of consequential decisions
the establishment of worker data councils; investment in closed-loop feedback that returns tangible benefit; periodic governance audits
7.2. Practical implications for industry
screening score · 2026-10-23
Privacy
4/5Protection of personal data and private life
limit collection to the strictly necessary; anonymise at the earliest stage; store identifier mappings separately under restricted access.
7.3.1. Organisational policy
screening score · 2026-10-23
Transparency
4/5Openness about how AI systems work and are used
transparent explanations of how monitoring interprets movement, modular and interoperable systems, open data standards
7.2. Practical implications for industry
screening score · 2026-10-23