From Human to Robot Interactions: A Circular Approach towards Trustworthy Social Robots
This paper proposes a circular approach to trust research in human-robot interactions, combining findings from human trust psychology with computational robot models. The authors suggest starting with human trust insights, testing them in human-robot interactions, developing robotic trust frameworks, and using reciprocal trust studies to generate new hypotheses about trust formation. By leveraging the manipulability of social robots, this interdisciplinary workflow aims to both improve trustworthy social robots and provide new insights into human biology and trust development.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers and academics in robotics, cognitive science, and psychology looking to understand trust formation in human-robot interactions.
What the document contains
The paper covers an interdisciplinary framework for studying trust in human-robot interactions by cycling insights between human psychology, robot implementation, computational trust models, and reciprocal trust studies.
The text does not provide legally binding governance regulations, policy guidelines, or enforcement mechanisms for deploying social robots.
- Trust research in human-robot interaction should follow a circular approach connecting human psychology and robotic implementation.
- Robots must transition from unknown entities to known entities through humanlike features and behaviors to gain trust.
- Cognitive load influences the level of trust both between humans and towards robots.
- Developing computational trust frameworks allows robots to assess the trustworthiness of their human or robotic partners.
- Evaluating how robotic trust frameworks affect human observer ratings is a critical next step for reciprocal trust building.
· I. INTRODUCTION · II. IMPLEMENTATION · III. DISCUSSION
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
screening score · 2026-10-23
Fairness
0/5Non-discrimination and equitable treatment
screening score · 2026-10-23
Human Dignity
0/5Respect for the inherent worth of persons
screening score · 2026-10-23
Human Oversight
0/5Humans remain in control of consequential decisions
screening score · 2026-10-23
Privacy
0/5Protection of personal data and private life
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
screening score · 2026-10-23