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Piggybacking Robots: Human-Robot Overtrust in University Dormitory Security

Published: 06 March 2017 Publication History

Abstract

Can overtrust in robots compromise physical security? We conducted a series of experiments in which a robot positioned outside a secure-access student dormitory asked passersby to assist it to gain access. We found individual participants were as likely to assist the robot in exiting the dormitory (40% assistance rate, 4/10 individuals) as in entering (19%, 3/16 individuals). Groups of people were more likely than individuals to assist the robot in entering (71%, 10/14 groups). When the robot was disguised as a food delivery agent for the fictional start-up Robot Grub, individuals were more likely to assist the robot in entering (76%, 16/21 individuals). Lastly, we found participants who identified the robot as a bomb threat demonstrated a trend toward assisting the robot (87%, 7/8 individuals, 6/7 groups). Thus, we demonstrate that overtrust---the unfounded belief that the robot does not intend to deceive or carry risk---can represent a significant threat to physical security at a university dormitory.

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cover image ACM Conferences
HRI '17: Proceedings of the 2017 ACM/IEEE International Conference on Human-Robot Interaction
March 2017
510 pages
ISBN:9781450343367
DOI:10.1145/2909824
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 06 March 2017

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Author Tags

  1. overtrust
  2. piggybacking
  3. robotics
  4. secure access
  5. tailgating
  6. trust

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HRI '17 Paper Acceptance Rate 51 of 211 submissions, 24%;
Overall Acceptance Rate 268 of 1,124 submissions, 24%

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  • (2024)Communication breakdown: Gaze-based prediction of system error for AI-assisted robotic arm simulated in VRProceedings of the 2024 Symposium on Eye Tracking Research and Applications10.1145/3649902.3653339(1-7)Online publication date: 4-Jun-2024
  • (2024)Encouraging Bystander Assistance for Urban Robots: Introducing Playful Robot Help-Seeking as a StrategyProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661505(2514-2529)Online publication date: 1-Jul-2024
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