Four Degree of Freedom Robotic Arm; An Ethical Analysis of Automation and Employment in the Manufacturing Industry
This academic capstone and Science, Technology, and Society (STS) research project examines the technical design of a four-degree-of-freedom handwriting robotic arm alongside an ethical analysis of manufacturing automation and employment. The technical portion details the fabrication of a low-backlash, closed-loop robotic arm capable of fine-motor handwriting tasks, while the STS portion investigates labor polarization, deskilling, and psychological strain on industrial workers. The study concludes that mitigating the negative impacts of automation requires organizations to integrate proactive upskilling programs during technology deployment.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, engineers, and academics interested in the intersection of robotic engineering design and the sociotechnical impacts of automation on the manufacturing workforce.
What the document contains
The document covers the technical design and physical prototyping of a four-degree-of-freedom robotic arm for handwriting, alongside an STS-based literature and data review examining the impact of manufacturing automation on labor polarization, deskilling, and workforce displacement.
The document does not provide formal AI governance regulations, legal frameworks, public policy directives, or enforcement mechanisms.
- Low-backlash cycloidal gearing combined with closed-loop feedback allows relatively low-cost robotic systems to execute fine-motor tasks such as handwriting.
- Manufacturing automation drives labor polarization, creating a barbell-shaped labor market split between low-wage service jobs and high-wage engineering positions.
- Transitioning industrial workers from active physical creation to passive screen monitoring can induce a decrease in job satisfaction and psychological strain.
- Organizations must integrate proactive upskilling programs directly into the deployment phase of new technologies to mitigate negative labor impacts.
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
0/5Clear responsibility for AI outcomes
Abstract
screening score · 2026-10-23
Fairness
2/5Non-discrimination and equitable treatment
the study analyzes the structural transition from manual execution to digital oversight. The results reveal that while automation increases production output, it hollows out mid-skill industrial roles, creating a "barbell" labor market split between low-wage service jobs and high-wage engineering positions.
Abstract
screening score · 2026-10-23
Human Dignity
2/5Respect for the inherent worth of persons
Furthermore, the findings demonstrate that transitioning workers from active physical creation to passive screen monitoring induces a decrease in job satisfaction.
Abstract
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
Abstract
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
Abstract
screening score · 2026-10-23
Transparency
0/5Openness about how AI systems work and are used
Abstract
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
Abstract
screening score · 2026-10-23
Impact Assessment
3/5Required assessment of an AI system's risks/impacts before or during use
Engineers must evaluate not only what a system can accomplish, but how its deployment alters human labor, organizational structures, and societal well-being.
Abstract
screening score · 2026-10-23