ABSTRACT
Previous research has shown that robot mistakes or malfunctions have a significant negative impact on people’s trust. One way to mitigate the negative impact of trust violation is through trust repair. Although trust repair has been studied extensively, it is still not known which strategy is effective in repairing trust in a time-sensitive driving scenario. Additionally, prior research on trust repair has not dealt with the effects of expressing emotion in attempting trust repair. In this paper, we presented the development of a variety of trust repair methods for a time-sensitive scenario using a simulated driving environment as a testbed for validation. These trust repair methods included baseline apology, emotional apology, and explanation. We conducted an experiment to compare the impact of these trust repair methods on human-robot trust. Experimental results indicated that the emotional apology positively affected more participants than the no-repair, baseline apology, and explanation. Furthermore, this study identified emotional apology as the most effective method for the time-sensitive driving scenario.
- [1]. , “Overtrust of robots in emergency evacuation scenarios,” in Human-Robot Interaction (HRI), 2016 11th ACM/IEEE International Conference on, 2016: IEEE, pp. 101–108. Google Scholar
- [2]. , “The Impact of First Impressions on Human-Robot Trust During Problem-Solving Scenarios,” in 2018 27th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), 2018: IEEE, pp. 435–441. Google Scholar
- [3]. , “How much do you trust your self-driving car? Exploring human-robot trust in high-risk scenarios,” in 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020: IEEE, pp. 4273–4280. Google Scholar
- [4]. , “Do you still trust me? human-robot trust repair strategies,” in 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN), 2021: IEEE, pp. 183–188. Google Scholar
- [5]. , “Trust repair strategies with self-driving vehicles: An exploratory study,” in Proceedings of the human factors and ergonomics society annual meeting, 2018, vol. 62, no. 1: SAGE Publications Sage CA: Los Angeles, CA, pp. 1108–1112. Google Scholar
- [6]. , “Timing is key for robot trust repair,” in International conference on social robotics, 2015: Springer, pp. 574–583. Google Scholar
- [7]. , “"I Don't Believe You”: Investigating the Effects of Robot Trust Violation and Repair," in 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2019: IEEE, pp. 57–65. Google Scholar
- [8]. , “Toward an understanding of trust repair in human-robot interaction: current research and future directions,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 8, no. 4, pp. 1–30, 2018. Google ScholarDigital Library
- [9]. ., “Taxonomy of Trust-Relevant Failures and Mitigation Strategies,” in Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020, pp. 3–12. Google Scholar
- [10]. , “When Should a Robot Apologize? Understanding How Timing Affects Human-Robot Trust Repair,” in International conference on social robotics, 2018: Springer, pp. 265–274. Google Scholar
- [11]. , “Fool Me Three Times: Trust Repair & Trustworthiness Over Multiple Violations and Repairs,” presented at the Cooperative AI NeurIPS Workshop, Virtual, 2021. Google Scholar
- [12]. , “Trust repair in human-agent teams: the effectiveness of explanations and expressing regret,” Autonomous Agents and Multi-Agent Systems, vol. 35, no. 2, pp. 1–20, 2021. Google Scholar
- [13]. , “Trust calibration within a human-robot team: Comparing automatically generated explanations,” in 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI), 2016: IEEE, pp. 109–116. Google Scholar
- [14]. , “"An Error Occurred!”-Trust Repair With Virtual Robot Using Levels of Mistake Explanation," in Proceedings of the 9th International Conference on Human-Agent Interaction, 2021, pp. 218–226. Google Scholar
- [15]. , “Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction,” in Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction, 2020, pp. 33–42. Google Scholar
- [16]. , “How should intelligent agents apologize to restore trust? Interaction effects between anthropomorphism and apology attribution on trust repair,” Telematics and Informatics, vol. 61, p. 101595, 2021. Google ScholarCross Ref
- [17]. , “Robot apology as a post-accident trust-recovery control strategy in industrial human-robot interaction,” International Journal of Industrial Ergonomics, vol. 82, p. 103078, 2021. Google ScholarCross Ref
- [18]. , “Feeling and believing: the influence of emotion on trust,” Journal of personality and social psychology, vol. 88, no. 5, p. 736, 2005. Google ScholarCross Ref
- [19]. , “How betrayal affects emotions and subsequent trust,” The Open Psychology Journal, vol. 8, pp. 153–159, 2015. Google ScholarCross Ref
- [20]. , “Do Integral Emotions Affect Trust? The Mediating Effect of Emotions on Trust in the Context of Human-Agent Interaction,” in Designing Interactive Systems Conference 2021, 2021, pp. 1492–1503. Google Scholar
- [21]. , “Emotions evoked by common words and phrases: Using mechanical turk to create an emotion lexicon,” in Proceedings of the NAACL HLT 2010 workshop on computational approaches to analysis and generation of emotion in text, 2010, pp. 26–34. Google Scholar
- [22]. , “A general psychoevolutionary theory of emotion,” in Theories of emotion: Elsevier, 1980, pp. 3–33. Google Scholar
- [23]. , “Saving face? When emotion displays during public apologies mitigate damage to organizational performance,” Organizational Behavior and Human Decision Processes, vol. 130, pp. 1–12, 2015. Google Scholar
- [24]. , “Apologies and medical error,” Clinical orthopaedics and related research, vol. 467, no. 2, pp. 376–382, 2009. Google Scholar
- [25]. , “An exploration of the structure of effective apologies,” Negotiation and Conflict Management Research, vol. 9, no. 2, pp. 177–196, 2016. Google Scholar
- [26]. . “Tone Analyzer.” https://tone-analyzer-demo.ng.bluemix.net/ (accessed 3/20, 2022). Google Scholar
- [27]. , “Recognizing language and emotional tone from music lyrics using IBM Watson Tone Analyzer,” in 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), 2019: IEEE, pp. 1–6. Google Scholar
- [28]. , “An IBM Watson Tone Analysis of Selected Judicial Decisions,” Scribes J. Leg. Writing, vol. 19, p. 25, 2020. Google Scholar
- [29]. , “Emotional tones in scientific writing: comparison of commercially funded studies and non-commercially funded orthopedic studies,” Acta Orthopaedica, vol. 92, no. 2, pp. 240–243, 2021. Google Scholar
- [30]. , “Awe the audience: How the narrative trajectories affect audience perception in public speaking,” in Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems, 2018, pp. 1–12. Google Scholar
- [31]. , “Gender differences in emotion perception and self-reported emotional intelligence: A test of the emotion sensitivity hypothesis,” PloS one, vol. 13, no. 1, p. e0190712, 2018. Google Scholar
- [32]. , “Perceived negative emotion in neutral faces: Gender-dependent effects on attractiveness and threat,” Emotion, vol. 19, no. 8, p. 1490, 2019. Google Scholar
- [33]. ., “Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI,” Information fusion, vol. 58, pp. 82–115, 2020. Google ScholarDigital Library
- [34]. , “Explanation in artificial intelligence: Insights from the social sciences,” Artificial intelligence, vol. 267, pp. 1–38, 2019. Google ScholarCross Ref
- [35]. , “Effect of Robot Performance on Human–Robot Trust in Time-Critical Situations,” IEEE Transactions on Human-Machine Systems, 2017. Google Scholar
- [36]. , “Foundations for an Empirically Determined Scale of Trust in Automated Systems,” International Journal of Cognitive Ergonomics, vol. 4, no. 1, pp. 53–71, 2000/03/01 2000, doi: 10.1207/S15327566IJCE0401_04. Google ScholarCross Ref
Comments