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Effects of Social Behaviors of Robots in Privacy-Sensitive Situations

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Abstract

In this paper, we investigate the effect of robot social behaviors in privacy-sensitive situations that are designed to reduce concerns of users regarding privacy invasion. As social robots ought to spend considerable time with humans, there has been research on user privacy protection. However, previous work mostly focuses on technical approaches not to disclose private information such as video/audio recordings and personally identifiable information. Although social robots could protect the privacy of users using those technologies, the users may still feel uncomfortable if they do not notice such the protection techniques being held. We design and test several behaviors of a social robot that can help users understand the internal process of the robot protecting user privacy so that users could have less concerns about their privacy. We choose three categories for the behavior design based on previous studies in psychology and human–robot interaction. They are (i) the gaze of the robot, (ii) the distance between the robot and a user, and (iii) clarity in expressing intent of the robot. In each category, we design and test three behaviors including a baseline (default) behavior. We conduct user studies with 56 participants in two scenarios to find effective behaviors. In the two scenarios, participants are asked to change clothes and write personal information in the presence of the robot in the vicinity, respectively. From the result, we find that users feel more comfortable in privacy-sensitive situations if they observe the robot perfor-ming the behaviors respecting user privacy. The most effective behavior of the robot is shown to be turning around from the user and showing robot’s back. As the robot combines more behaviors (such as telling the user that video recording is suspended, moving away from the user, and then turning around), the concern of users tends to decrease.

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Notes

  1. We assume that the data containing body exposure include the face or other features of the body. Thus, there is a risk that the person can be identified by the data.

  2. In [15], it is discussed that LED indicators are not noticeable and understandable to humans.

  3. The two participants had the same order of behaviors. It was Low-High-Baseline-Low-High-Baseline where the first three were for the first scenario and the rest is for the second scenario. Since they did not notice the change for all two Low’s, they seemed to have higher threshold value for noticing visual changes than other participants.

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Funding

This study was funded by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST) and INHA UNIVERSITY Research Grant.

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Authors and Affiliations

  1. Koh Young Technology, Seoul, Korea

    Daseul Yang

  2. Korea Institute of Science and Technology (KIST), Seoul, Korea

    Yu-Jung Chae, Yoonseob Lim, Dong Hwan Kim, ChangHwan Kim & Sung-Kee Park

  3. Seoul Robotics Co., Ltd, Seoul, Korea

    Doogon Kim

  4. Inha University, Incheon, Korea

    Changjoo Nam

  5. Department of HY-KIST Bio-convergence, Hanyang University, Seoul, Korea

    Yoonseob Lim

Authors
  1. Daseul Yang
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  2. Yu-Jung Chae
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  3. Doogon Kim
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  4. Yoonseob Lim
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  5. Dong Hwan Kim
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  6. ChangHwan Kim
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  7. Sung-Kee Park
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  8. Changjoo Nam
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Correspondence to Changjoo Nam.

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This work was supported in part by the National Research Council of Science & Technology (NST) grant by the Korea government (MSIP) (No. CRC-15-04-KIST) and INHA UNIVERSITY Research Grant. The IRB approval no. is KIST No. 2019-019.

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Yang, D., Chae, YJ., Kim, D. et al. Effects of Social Behaviors of Robots in Privacy-Sensitive Situations. Int J of Soc Robotics 14, 589–602 (2022). https://doi.org/10.1007/s12369-021-00809-2

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