Abstract
Explanations are a useful tool to improve human-robot interaction and the topic of what a good explanation should entail has received much attention. While a robot’s behavior can be justified upon request after its execution, the intention to act can also be signaled by a robot prior to the execution. In this paper we report results from a pre-registered study on the effects of a social robot proactively giving a self-explanation before vs. after the execution of an undesirable behavior. Contrary to our expectations we found that explaining a behavior before its execution did not yield positive effects on the users’ perception of the robot or the behavior. Instead, the robot’s behavior was perceived as less desirable when explained before the execution rather than afterwards. Exploratory analyses further revealed that even though participants felt less uncertain about what was going to happen next, they also felt less in control, had lower trust and lower contact intentions with a robot that explained before it acted.
This research was supported by the German Federal Ministry of Education and Research (BMBF) in the project ‘VIVA’ (FKZ 16SV7959).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
- 2.
Please note that this enumeration is partially inconsistent with the pre-registered hypothesis.
- 3.
Original videos: https://dl.acm.org/doi/abs/10.1145/3319502.3374802#sec-supp.
- 4.
- 5.
- 6.
References
Anjomshoae, S., Najjar, A., Calvaresi, D., Främling, K.: Explainable agents and robots: Results from a systematic literature review. In: 18th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2019), Montreal, Canada, May 13–17, 2019, pp. 1078–1088. International Foundation for Autonomous Agents and Multiagent Systems (2019)
Baraka, K., Paiva, A., Veloso, M.: Expressive lights for revealing mobile service robot state. In: Robot 2015: Second Iberian Robotics Conference. AISC, vol. 417, pp. 107–119. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27146-0_9
Bartneck, C., Kulić, D., Croft, E., Zoghbi, S.: Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. Int. J. Soc. Robot. 1(1), 71–81 (2009)
Besold, T.R., Uckelman, S.L.: The what, the why, and the how of artificial explanations in automated decision-making. CoRR (2018)
Cha, E., Kim, Y., Fong, T., Mataric, M.J.: A survey of nonverbal signaling methods for non-humanoid robots. Found. Trends Robot. 6(4), 211–323 (2018)
Ehsan, U., Tambwekar, P., Chan, L., Harrison, B., Riedl, M.O.: Automated rationale generation: a technique for explainable ai and its effects on human perceptions. In: Proceedings of the 24th International Conference on Intelligent User Interfaces, pp. 263–274 (2019)
Eyssel, F., Kuchenbrandt, D.: Social categorization of social robots: anthropomorphism as a function of robot group membership. Br. J. Soc. Psychol. 51(4), 724–731 (2012)
Eyssel, F., Loughnan, S.: “It Don’t Matter If You’re Black or White’’? In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.) ICSR 2013. LNCS (LNAI), vol. 8239, pp. 422–431. Springer, Cham (2013). https://doi.org/10.1007/978-3-319-02675-6_42
Faul, F., Erdfelder, E., Lang, A.G., Buchner, A.: G* power 3: a flexible statistical power analysis program for the social, behavioral, and biomedical sciences. Behav. Res. Methods 39(2), 175–191 (2007)
Hilton, D.J.: Conversational processes and causal explanation. Psychol. Bull. 107(1), 65 (1990)
Lyons, J.B., et al.: Shaping trust through transparent design: theoretical and experimental guidelines. In: Savage-Knepshield, P., Chen, J. (eds.) Advances in Human Factors in Robots and Unmanned Systems, vol. 499, pp. 127–136. Springer, Basel, Switzerland (2017). https://doi.org/10.1007/978-3-319-41959-6_11
Pipitone, A., Chella, A.: What robots want? hearing the inner voice of a robot. Iscience 24(4), 102371 (2021)
Priester, J.R., Petty, R.E.: The gradual threshold model of ambivalence: relating the positive and negative bases of attitudes to subjective ambivalence. J. Personal. Soc. Psychol. 71(3), 431 (1996)
Putnam, V., Conati, C.: Exploring the need for explainable artificial intelligence (XAI) in intelligent tutoring systems (ITS). In: CEUR Workshop Proceedings, pp. 23–27 (2019)
Reich-Stiebert, N., Eyssel, F.: Learning with educational companion robots? toward attitudes on education robots, predictors of attitudes, and application potentials for education robots. Int. J. Soc. Robot. 7(5), 875–888 (2015)
Reysen, S.: Construction of a new scale: the reysen likability scale. Soc. Behav. Personal. Int. J. 33(2), 201–208 (2005)
Rosenfeld, A., Richardson, A.: Explainability in human-agent systems. Auton. Agents Multi-Agent Syst. 33(6), 673–705 (2019)
Stange, S., Kopp, S.: Effects of a social robot’s self-explanations on how humans understand and evaluate its behavior. In: Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, pp. 619–627 (2020)
Stange, S., Kopp, S.: Effects of referring to robot vs. user needs in self-explanations of undesirable robot behavior. In: Companion of the 2021 ACM/IEEE International Conference on Human-Robot Interaction, pp. 271–275 (2021)
Touré-Tillery, M., McGill, A.L.: Who or what to believe: trust and the differential persuasiveness of human and anthropomorphized messengers. J. Market. 79(4), 94–110 (2015)
Walton, D.: A new dialectical theory of explanation. Philos. Explor. 7(1), 71–89 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Stange, S., Kopp, S. (2021). Explaining Before or After Acting? How the Timing of Self-Explanations Affects User Perception of Robot Behavior. In: Li, H., et al. Social Robotics. ICSR 2021. Lecture Notes in Computer Science(), vol 13086. Springer, Cham. https://doi.org/10.1007/978-3-030-90525-5_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-90525-5_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-90524-8
Online ISBN: 978-3-030-90525-5
eBook Packages: Computer ScienceComputer Science (R0)