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Effects of Proactive Explanations by Robots on Human-Robot Trust

Part of the Lecture Notes in Computer Science book series (LNAI,volume 12483)

Abstract

The performance of human-robot teams depends on human-robot trust, which in turn depends on appropriate robot-to-human transparency. A key way for robots to build trust through transparency is by providing appropriate explanations for their actions. While most previous work on robot explanation generation has focused on robots’ ability to provide post-hoc explanations upon request, in this paper we instead examine proactive explanations generated before actions are taken, and the effect this has on human-robot trust. Our results suggest a positive relationship between proactive explanations and human-robot trust, and reveal fundamental new questions into the effects of proactive explanations on the nature of humans’ mental models and the fundamental nature of human-robot trust.

Keywords

  • Human-robot interaction
  • Human-robot trust
  • Transparency
  • Explanation

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Acknowledgments

This work was supported by an Early Career Faculty grant from NASA’s Space Technology Research Grants Program.

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Correspondence to Lixiao Zhu .

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Zhu, L., Williams, T. (2020). Effects of Proactive Explanations by Robots on Human-Robot Trust. In: , et al. Social Robotics. ICSR 2020. Lecture Notes in Computer Science(), vol 12483. Springer, Cham. https://doi.org/10.1007/978-3-030-62056-1_8

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  • DOI: https://doi.org/10.1007/978-3-030-62056-1_8

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