Privacy-Sensitive Robotics: Perceptions, Measures, and Metrics

As robots enter our homes and workplaces, they will have more direct access to us, our information, and our daily lives. The more a robot knows about us, the more helpful it can be, at least in theory. Where is the balance between privacy and utility? How might the robot communicate the tradeoffs between privacy and utility in a nuanced but clear manner? How can we ensure that the perception of privacy-protection offered by the robot accurately reflects how much the robot is actually preserving our privacy? How can a robot learn information that it can use to better help us without being intrusive? What behaviors should the robot have (or not have) to not only ensure that it protects our privacy, but also is perceived as protecting our privacy? How can we measure the effectiveness of these behaviors, so that we can track our progress towards more useful, less invasive robot companions? This workshop is the the third in a series at HRI, and will bring together researchers from a wide variety of intellectual communities to look at these questions, identify promising research directions, and set an agenda for how to start making progress. We are particularly interested in expanding the core of researchers interested in privacy-related issues in HRI, and in those “privacy-curious” researchers who want to find out more about he intersection of privacy and their own research.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974048
Citations
2
Primary Topic
Ethics and Social Impacts of AI
Type
article
Field-Weighted Citation Impact
4.73
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article

Privacy-Sensitive Robotics: Perceptions, Measures, and Metrics

Janet Kim, William D. Smart, Jun Ota, Leigh Levinson et al.
2 citations
Ethics and Social Impacts of AI
4.73
article

Privacy-Sensitive Robotics: Perceptions, Measures, and Metrics

Janet Kim, William D. Smart, Jun Ota, Leigh Levinson, Selma Šabanović
article en
2 citations

Abstract

As robots enter our homes and workplaces, they will have more direct access to us, our information, and our daily lives. The more a robot knows about us, the more helpful it can be, at least in theory. Where is the balance between privacy and utility? How might the robot communicate the tradeoffs between privacy and utility in a nuanced but clear manner? How can we ensure that the perception of privacy-protection offered by the robot accurately reflects how much the robot is actually preserving our privacy? How can a robot learn information that it can use to better help us without being intrusive? What behaviors should the robot have (or not have) to not only ensure that it protects our privacy, but also is perceived as protecting our privacy? How can we measure the effectiveness of these behaviors, so that we can track our progress towards more useful, less invasive robot companions? This workshop is the the third in a series at HRI, and will bring together researchers from a wide variety of intellectual communities to look at these questions, identify promising research directions, and set an agenda for how to start making progress. We are particularly interested in expanding the core of researchers interested in privacy-related issues in HRI, and in those “privacy-curious” researchers who want to find out more about he intersection of privacy and their own research.

Amazon (United States) (US), Oregon State University (US), Indiana University Bloomington (US)
Openalex Percentile: Top 6%
Ethics and Social Impacts of AI
4.73
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