A Thematic Coding Framework for Ethical and Safety Challenges in Collaborative Robotics in Manufacturing: A Systematic Approach
This academic study presents a systematic approach to identifying, categorizing, and analyzing ethical and safety challenges in collaborative robotics within manufacturing. Utilizing a mixed-methods framework analyzing 3,390 publications, the study introduces a hierarchical thematic coding framework encompassing safety, ethical, and implementation challenges. The findings highlight high prevalences of ethical and safety considerations, such as job displacement and psychological safety, while pointing out research gaps like sensor reliability.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and practitioners looking to navigate ethical and safety issues in collaborative manufacturing robot design.
What the document contains
The document covers a systematic literature review and bibliometric assessment of ethical, safety, and implementation challenges related to human-robot collaboration in manufacturing environments.
It does not provide binding regulations, mandatory compliance codes, or specific legal enforcement mechanisms for industrial robotics.
- The study develops a hierarchical thematic coding framework for ethical and safety challenges in collaborative manufacturing robots.
- Safety and ethical considerations were found in 81.9% and 91.2% of analyzed papers respectively.
- Key specific challenges identified include job displacement, psychological safety, and human autonomy.
- Notable research gaps exist in areas such as sensor reliability and decision authority.
Not stated
How it scores
Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.
Accountability
2/5Clear responsibility for AI outcomes
Key specific challenges include job displacement (75.4%), psychological safety (45.7%), and human autonomy (43.9%), whereas notable research gaps exist in areas such as sensor reliability (0.7%) and decision authority (0.2%).
Abstract
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
Abstract
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
Abstract
screening score · 2026-10-24
Human Oversight
2/5Humans remain in control of consequential decisions
Key specific challenges include job displacement (75.4%), psychological safety (45.7%), and human autonomy (43.9%), whereas notable research gaps exist in areas such as sensor reliability (0.7%) and decision authority (0.2%).
Abstract
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
Abstract
screening score · 2026-10-24
Transparency
0/5Openness about how AI systems work and are used
Abstract
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
Abstract
screening score · 2026-10-24
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
We employed a comprehensive mixed-methods framework that integrated systematic literature analysis, thematic content evaluation, and quantitative bibliometric assessment
Abstract
screening score · 2026-10-24