Characteristics of a responsible community robot: Data from a Delphi study
This data article presents a dataset collected through a six-round Delphi study to identify design characteristics for a responsible community robot, using a waterway monitoring robot as a case study. The study engaged twenty multidisciplinary panelists who contributed ideas that were distilled into 111 voting statements and rated from essential to unnecessary. The published dataset includes raw questionnaires, demographic data, intermediate study analyses, and supporting materials like transcripts and codebooks. This resource aims to support researchers, roboticists, and community groups investigating responsible robotics and stakeholder-engaged design.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, roboticists, and technologists interested in responsible robotics, stakeholder engagement, and consensus measurement approaches.
What the document contains
The document covers the dataset description, methodology, and experimental design of a Delphi study focused on determining the characteristics of responsible community robots. It outlines participant recruitment, phase structures including demographics, ideation, and iterative voting, and data analysis techniques using consensus and stability measures.
The text does not provide binding legal regulations, formal technical safety standards, or direct enforcement mechanisms for robotics.
- A six-round Delphi study involving 20 expert panelists was conducted to identify characteristics defining a responsible community robot.
- The study utilized a case study of a waterway monitoring robot designed to assist citizen scientists.
- Panelists contributed 170 initial ideas, which were combined into 111 voting statements and rated across Likert scales.
- The published dataset contains raw demographic, ideation, and voting data along with transcripts, codebooks, and analysis files.
· Value of the Data · Background · Data Description · Experimental Design, Materials and Methods - Study scenario and scope - Panel selection and recruitment - Study design - Data collection - Data analysis · Limitations · Ethics Statement · CRediT Author Statement · References · Data Availability
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
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Fairness
0/5Non-discrimination and equitable treatment
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Human Dignity
0/5Respect for the inherent worth of persons
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Human Oversight
0/5Humans remain in control of consequential decisions
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Privacy
0/5Protection of personal data and private life
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Transparency
0/5Openness about how AI systems work and are used
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Enforcement
0/5Consequences and compliance mechanisms with real force
4. Experimental Design, Materials and Methods
screening score · 2026-10-24
Impact Assessment
0/5Required assessment of an AI system's risks/impacts before or during use
4. Experimental Design, Materials and Methods
screening score · 2026-10-24