Record STMT-WB73782Voluntary

Trust Regulation in Social Robotics: From Violation to Repair

Trust regulation in Social Robotics: From Violation to Repair

Report Institutional policyNorthern Europe · DKadopted

This research paper evaluates different approaches to trust calibration in human-robot interaction through an empirical study where participants teamed up with a social robot. The experiment utilized five strategies including proficiency, situation awareness, transparency, trust violation, and trust repair during a collaborative game. Results confirmed that subjective levels of trust can be effectively manipulated, violated, and repaired over the course of an interaction.

Summary and content fields are drawn from the document text and are pending editorial review.

Who issued it

Association for Computing MachineryOther

Publisher

Legal forceVoluntary

Under whose authorityInstitutional policy

Judged fromfull text

Who it governs

Researchers and academicsGeneral publicAI developers and providersAI deployers and users

Researchers and developers interested in human-robot interaction, trust calibration, and social robotics dynamics should read this paper.

Sectors

Research and science · human-robot interaction research

What the document contains

What's covered

The paper covers an empirical evaluation of five trust calibration strategies—proficiency, situation awareness, transparency, trust violation, and trust repair—in human-robot teams using a modified collaborative game.

What's missing

The text does not provide regulatory frameworks, legal compliance mandates, or legislative guidelines for social robotics.

Key takeaways

Subjective levels of trust in social robots can be effectively manipulated over the course of a single human-robot interaction.
Robot behaviors demonstrating situation awareness significantly increased perceived benevolence compared to baseline.
Trust violation strategies led to a significant drop in moral trust and overall trust compared to the initial stage.
An apology and trustworthy actions effectively repaired the subjective level of trust after a violation.

Outline

· 1 Introduction
· 2 Method
  - 2.1 Experiment design
    - 2.1.1 Candidates for trust calibration
    - 2.1.2 Experimental procedure
· 3 Results
· 4 Discussion and future work

Issued28 April 2023

Length2,593 words · 5 pages

FormatNot recorded

Scopenational

How it scores

Pending review

Each score is backed by a quote verified against the document text. Editorial review has not yet confirmed them.

Accountability

0/5

Clear responsibility for AI outcomes

screening score · 2026-10-24

Fairness

0/5

Non-discrimination and equitable treatment

screening score · 2026-10-24

Human Dignity

0/5

Respect for the inherent worth of persons

screening score · 2026-10-24

Human Oversight

0/5

Humans remain in control of consequential decisions

screening score · 2026-10-24

Privacy

0/5

Protection of personal data and private life

screening score · 2026-10-24

Transparency

3/5

Openness about how AI systems work and are used

Based on the theory outlined by [19], the section focusing on transparency (S3) employed a predictive and preventative approach to trust calibration when the robot informed the participant about the limitations of its capabilities to prepare them for a potential error.

2.1.1 Candidates for trust calibration

screening score · 2026-10-24

Enforcement

0/5

Consequences and compliance mechanisms with real force

screening score · 2026-10-24

Impact Assessment

0/5

Required assessment of an AI system's risks/impacts before or during use

screening score · 2026-10-24

Topics

human_robot_interaction trust_regulation social_robotics trust_repair empirical_study