Evaluating the Impact of a Personal Data Communication Policy in Human-Robot Interactions
This paper presents a novel personal data communication policy for home companion robots designed to mitigate the risk of inadvertent personal data exposure during human-robot interactions. The policy dynamically adjusts communication strategies based on the sensitivity level of shared data (low, medium, and high concern). Through an empirical hybrid study conducted online and in-person, the authors evaluate participant preferences and find that the adaptive policy minimizes data exposure and fosters increased trust in the robot's handling of sensitive information.
Summary and content fields are drawn from the document text and are pending editorial review.
Who issued it
Who it governs
Researchers, developers, and designers working in human-robot interaction and privacy-sensitive robotics.
What the document contains
This document covers the design, implementation, and empirical evaluation of a communication policy for social companion robots that adapts output methods based on personal data sensitivity. It assesses participant perceptions of trust, privacy, approval of personal data storage, and the presence of third parties during interactions.
The text does not provide formal regulatory guidelines, legal compliance frameworks, or binding standards for robot manufacturers.
- Companion robots operating in domestic environments require dynamic communication policies to prevent inadvertent personal data exposure.
- Implementing a classification-based communication policy tailored to data sensitivity significantly increases user trust in robotic data handling.
- An experimental policy where low-concern data is spoken aloud, medium-concern data prompts for consent, and high-concern data is sent directly to a phone was preferred by 84% of participants.
- Demonstrating secure personal data communication increases user approval for robots knowing and storing personal data for tailored experiences.
- Participant trust and approval ratings were largely unaffected by whether the robot used was humanoid or non-humanoid.
· I. Introduction · II. Methodology · III. Results · IV. Discussion · V. Conclusion
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
2/5Humans remain in control of consequential decisions
Unlike Human-Computer Interaction (HCI), where a device needs to be unlocked [9] or have some form of user intervention to communicate personal data.
Section I
screening score · 2026-10-23
Privacy
5/5Protection of personal data and private life
This paper presents a novel communication policy designed to mitigate such risks by dynamically adjusting communication strategies based on the sensitivity of the shared data.
Abstract
screening score · 2026-10-23
Transparency
3/5Openness about how AI systems work and are used
Low-concern personal data was said out loud in full; for medium-concern personal data, the robot asked the user whether they wanted it said out loud or sent to their phone; for high-concern personal data, these were sent directly to the user’s phone.
Section II
screening score · 2026-10-23
Enforcement
0/5Consequences and compliance mechanisms with real force
screening score · 2026-10-23
Impact Assessment
4/5Required assessment of an AI system's risks/impacts before or during use
This paper presents a novel communication policy designed to mitigate such risks by dynamically adjusting communication strategies based on the sensitivity of the shared data.
Abstract
screening score · 2026-10-23