Trust and Trustworthiness from Human-Centered Perspective in HRI – A Systematic Literature Review
TRUST AND TRUSTWORTHINESS FROM HUMAN -C ENTERED PERSPECTIVE IN HRI – A S YSTEMATIC LITERATURE REVIEW
This systematic literature review examines how trust and trustworthiness are conceptualized, assessed, and studied within Human-Robot Interaction (HRI) literature, particularly in the context of Industry 5.0. Analyzing 34 selected articles following PRISMA guidelines, the authors find a significant lack of conceptual clarity regarding trust—with nearly 30 percent of reviewed studies lacking a formal definition—which contributes to contradictory findings. The review also categorizes common methodologies, deployment domains, and the primary facilitators and barriers influencing user trust in autonomous robotic systems.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
This document is for researchers and developers seeking an overview of trust dynamics, methodologies, and barriers in human-robot interaction.
What the document contains
The document covers a systematic literature review of human-robot interaction studies, focusing on how trust is defined, measured, and influenced across various domains such as service robotics and industrial automation.
The document does not provide binding legal regulations, compliance checklists, or enforceable technical standards for autonomous system development.
- Nearly 30% of reviewed HRI trust studies fail to provide a formal definition of trust, creating conceptual ambiguity.
- Experimental studies make up the majority of research methods used to evaluate user trust in HRI.
- Robot performance, behavioral style, transparency, and human-like attributes act as primary facilitators of user trust.
- Technical failures, social norm violations, and ambiguous explanations function as major barriers to building and maintaining trust.
- Achieving sustainable human-robot collaboration requires tailored interactions that balance trust calibration without leading to misuse or disuse.
· Introduction · Methodology · Analysis & Results · Conclusions · Future considerations · Limitations
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
This ensures that critical decisions are aligned with ethical, contextual, and situational requirements, and humans are ethically accountable for their choices, such as AI-generated clinic diagnoses.
1.1.1 Industry 5.0 vision
screening score · 2026-10-24
Fairness
1/5Non-discrimination and equitable treatment
Other regions are represented by fewer studies, including Northern Europe (N=2), with studies from Sweden and Norway. Australasia (N=2), primarily Australia, and Southern Europe (N=2), including Spain and Portugal.
3 Analysis & Results
screening score · 2026-10-24
Human Dignity
2/5Respect for the inherent worth of persons
In sum, the objectives envisioned by Industry 5.0 contemplate that technologies adapt to human needs and the diversity of human nature, empower workers, and improve their process efficiency rather than replace them Nahavandi (2019).
1 Introduction
screening score · 2026-10-24
Human Oversight
3/5Humans remain in control of consequential decisions
Enabling tools to augment the human ability to be creative and solve problems, benefiting from machine precision and data processing while preserving the human role in critical decision-making processes.
1 Introduction
screening score · 2026-10-24
Privacy
2/5Protection of personal data and private life
Characteristics of user privacy orientation and perceived privacy are emphasized by the works of Gulati et al. (2019) and Ambsdorf et al. (2022).
3.4 What are the barriers and facilitators for fostering trustworthy HRI?
screening score · 2026-10-24
Transparency
3/5Openness about how AI systems work and are used
Alonso and Puente (2018) offered a meta-analysis on transparency in the framework of shared autonomy.
3.3 What has been the focus of HRI researchers when investigating trust?
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
4 Conclusions
screening score · 2026-10-24
Impact Assessment
1/5Required assessment of an AI system's risks/impacts before or during use
We evaluated the quality of the selected articles using a mixed methods tool, then extracted data to meet the review’s objectives.
2.2 Screening
screening score · 2026-10-24