An ethical framework for human-robot collaboration for the future people-centric manufacturing: A collaborative endeavour with European subject-matter experts in ethics
Why this work is in the frame
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Bibliographic record
Abstract
Envisioning humans and (smart) robots collaboratively working on the manufacturing shop floor, sharing spaces, tasks and objectives, reflects the ambitious goal that the ideal factory of the future aspires to attain. However, ensuring the effective implementation of this novel form of labour organisation remains an ongoing area of research. Key aspects such as the future role of workers, potential psychological risks, and the overall ethical considerations of human-robot (H-R) collaboration warrant further investigation until the underpinning safety challenges have been addressed. This study presents a novel ethical framework for H-R collaboration in manufacturing, which involved 30 subject-matter experts in ethics within the European context in a collaborative design process conducted through a year-long three-round data collection qualitative Delphi study. The ethical framework adopts a human-centric approach, recognising the influences that expand beyond the specific context of H-R dynamics on the shop floor, towards organisational and societal governance for a more responsible integration of (smart) robotics into the professional settings. Ethics, in this regard, aims to foster ethical awareness and accountability in the processes and practices of design and innovation, involving all stakeholders who play a role in shaping the future of Industry 5.0. • This framework is the results of a year-long, three-round qualitative Delphi approach with 30 European ethicists. • Ethical principles were co-defined to address H-R collaboration challenges both on the shop floor and at organisational level. • Three ethical principles on the shop floor focus on human autonomy, (decision) authority and agency. • The organisational governance for H-R collaboration includes six ethical principles. • These principles cover human dignity, liability, safety, GDPR principles, unbiased ML/AI data, and human resilience.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it