Do You Still Trust Me? Human-Robot Trust Repair Strategies
Open accessAbstract
Trust is vital to promoting human and robot collaboration, but like human teammates, robots make mistakes that undermine trust. As a result, a human’s perception of his or her robot teammate’s trustworthiness can dramatically decrease [1], [2], [3], [4]. Trustworthiness consists of three distinct dimensions: ability (i.e. competency), benevolence (i.e. concern for the trustor) and integrity (i.e. honesty) [5], [6]. Taken together, decreases in trustworthiness decreases trust in the robot [7]. To address this, we conducted a 2 (high vs. low anthropomorphism) x 4 (trust repair strategies) between-subjects experiment. Preliminary results of the first 164 participants (between 19 and 24 per cell) highlight which repair strategies are effective relative to ability, integrity and benevolence and the robot’s anthropomorphism. Overall, this paper contributes to the HRI trust repair literature.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Results of Fahim et al. and Esterwood and Robert indicate that interventions affected the trust subconstructs differently.
“Results of both papers indicate that interventions afected the trust subconstructs diferently.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [35]
Esterwood and Robert found perceptions of a system's abilities were generally unaffected by any repair strategy; repair strategies increased perceived benevolence and integrity.
“For example, Esterwood and Robert [35] found that perceptions of a system's abilities were generally unafected by any repair strategy.In contrast, repair strategies increased the perceived benevolence and integrity of a system.”
From Measuring and Understanding Trust Calibrations for Automated Systems: A Survey of the State-Of-The-Art and Future Directions (2023) · cites this paper as [35]
Cites
An integrative model of organizational trust
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995) · The Academy of Management Review · doi:10.2307/258792 · Cited by 10,736 · see citing works
A Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction
Hancock, P. A., Billings, D. R., Schaefer, K. E., Chen, J. Y. C., de Visser, E. J., & Parasuraman, R. (2011) · Human Factors: The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720811417254 · Cited by 2,051 · cited 1× · see citing works
“Answering this question is important because each element’s impacts can vary greatly in a given context [21], [22], [23].”
BackgroundTrust in a specific technology: An investigation of its components and measures
Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011) · ACM Transactions on Management Information Systems · doi:10.1145/1985347.1985353 · Cited by 1,185 · see citing works
Kim, P. H., Ferrin, D. L., Cooper, C. D., & Dirks, K. T. (2004) · Journal of Applied Psychology · doi:10.1037/0021-9010.89.1.104 · Cited by 1,077 · see citing works
Promises and lies: Restoring violated trust
Schweitzer, M. E., Hershey, J. C., & Bradlow, E. T. (2006) · Organizational Behavior and Human Decision Processes · doi:10.1016/j.obhdp.2006.05.005 · Cited by 548 · see citing works
Trust in humanoid robots: implications for services marketing
van Pinxteren, M. M. E., Wetzels, R. W. H., Rüger, J., Pluymaekers, M., & Wetzels, M. G. M. (2019) · Journal of Services Marketing · doi:10.1108/jsm-01-2018-0045 · Cited by 544 · see citing works
Individual Swift Trust and Knowledge-Based Trust in Face-to-Face and Virtual Team Members
Robert, L. P., Denis, A. R., & Hung, Y.-T. C. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222260210 · Cited by 514 · see citing works
Self-citationSalem, M., Lakatos, G., Amirabdollahian, F., & Dautenhahn, K. (2015) · Proceedings of the Tenth Annual ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/2696454.2696497 · Cited by 494 · see citing works
From ‘automation’ to ‘autonomy’: the importance of trust repair in human–machine interaction
de Visser, E. J., Pak, R., & Shaw, T. H. (2018) · Ergonomics · doi:10.1080/00140139.2018.1457725 · Cited by 416 · see citing works
Repairing and Enhancing Trust: <i>Approaches to Reducing Organizational Trust Deficits</i>
Kramer, R. M., & Lewicki, R. J. (2010) · Academy of Management Annals · doi:10.1080/19416520.2010.487403 · Cited by 398 · see citing works
Underwood, L. G. (2004) · DigitalGeorgetown (Georgetown University Library) · doi:10.4135/9781412958479 · Cited by 367 · see citing works
Eyssel, F., Kuchenbrandt, D., Bobinger, S., de Ruiter, L., & Hegel, F. (2012) · Proceedings of the seventh annual ACM/IEEE international conference on Human-Robot Interaction · doi:10.1145/2157689.2157717 · Cited by 307 · see citing works
Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction
Natarajan, M., & Gombolay, M. (2020) · Proceedings of the 2020 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3319502.3374839 · Cited by 248 · see citing works
Phillips, E., Zhao, X., Ullman, D., & Malle, B. F. (2018) · Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3171221.3171268 · Cited by 233 · see citing works
Trust calibration within a human-robot team: Comparing automatically generated explanations
Wang, N., Pynadath, D. V., & Hill, S. G. (2016) · 2016 11th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2016.7451741 · Cited by 220 · see citing works
Lewicki, R. J., & Brinsfield, C. (2017) · Annual Review of Organizational Psychology and Organizational Behavior · doi:10.1146/annurev-orgpsych-032516-113147 · Cited by 202 · see citing works
You, S., Kim, J., Lee, S. H., Kamat, V. R., & Robert, L. (2018) · Automation in Construction · doi:10.1016/j.autcon.2018.09.008 · Cited by 194 · see citing works
Self-citationDu, N., Haspiel, J., Zhang, Q., Tilbury, D. M., Pradhan, A. K., Yang, X. J., & Robert, L. (2019) · Transportation Research Part C Emerging Technologies · doi:10.1016/j.trc.2019.05.025 · Cited by 174 · see citing works
Self-citationToward an Understanding of Trust Repair in Human-Robot Interaction
Baker, A. L., Phillips, E. K., Ullman, D. L., & Keebler, J. R. (2018) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3181671 · Cited by 165 · see citing works
The Relationship Between Being Perceived as Trustworthy by Coworkers and Individual Performance†
Dirks, K. T., & Skarlicki, D. P. (2007) · Journal of Management · doi:10.1177/0149206308321545 · Cited by 163 · see citing works
The (Fe)male Robot: How Robot Body Shape Impacts First Impressions and Trust Towards Robots
Bernotat, J., Eyssel, F., & Sachse, J. (2019) · International Journal of Social Robotics · doi:10.1007/s12369-019-00562-7 · Cited by 142 · see citing works
Timing Is Key for Robot Trust Repair
Robinette, P., Howard, A. M., & Wagner, A. R. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-25554-5_57 · Cited by 121 · see citing works
Human–robot similarity and willingness to work with a robotic co-worker
You, S., & Robert Jr., L. P. (2018) · Proceedings of the 2018 ACM/IEEE International Conference on Human-Robot Interaction · doi:10.1145/3171221.3171281 · Cited by 118 · see citing works
Self-citation“I Don't Believe You”: Investigating the Effects of Robot Trust Violation and Repair
Sebo, S. S., Krishnamurthi, P., & Scassellati, B. (2019) · 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri.2019.8673169 · Cited by 110 · see citing works
Kim, W., Kim, N., Lyons, J. B., & Nam, C. S. (2020) · Applied Ergonomics · doi:10.1016/j.apergo.2020.103056 · Cited by 109 · see citing works
Trust theory and customer services research: theoretical review and synthesis
Isaeva, N., Gruenewald, K., & Saunders, M. N. K. (2020) · Service Industries Journal · doi:10.1080/02642069.2020.1779225 · Cited by 95 · see citing works
A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot Interaction
Esterwood, C. T., Essenmacher, K., Yang, H., Zeng, F., & Robert, L. (2021) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445542 · Cited by 89 · see citing works
Self-citationTrusting Robocop: Gender-Based Effects on Trust of an Autonomous Robot
Gallimore, D., Lyons, J. B., Vo, T. Q., Mahoney, S., & Wynne, K. T. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00482 · Cited by 84 · see citing works
Shape It – The Influence of Robot Body Shape on Gender Perception in Robots
Bernotat, J., Eyssel, F., & Sachse, J. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70022-9_8 · Cited by 78 · see citing works
Sharp, E. A., Thwaites, R., Curtis, A. L., & Millar, J. (2012) · Journal of Environmental Planning and Management · doi:10.1080/09640568.2012.717052 · Cited by 74 · see citing works
A Unified Bi-Directional Model for Natural and Artificial Trust in Human–Robot Collaboration
Azevedo-Sa, H., Yang, X. J., Robert, L., & Tilbury, D. M. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3088082 · Cited by 64 · see citing works
Self-citationWhen Would You Trust a Robot? A Study on Trust and Theory of Mind in Human-Robot Interactions
Mou, W., Ruocco, M., Zanatto, D., & Cangelosi, A. (2020) · IEEE RO-MAN · doi:10.1109/ro-man47096.2020.9223551 · Cited by 52 · see citing works
Mathur, M. B., & Reichling, D. B. (2009) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/1514095.1514192 · Cited by 49 · see citing works
Trusting Autonomous Security Robots: The Role of Reliability and Stated Social Intent
Lyons, J. B., Vo, T. Q., Wynne, K. T., Mahoney, S., Nam, C. S., & Gallimore, D. M. (2020) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720820901629 · Cited by 49 · see citing works
The Impact of First Impressions on Human- Robot Trust During Problem-Solving Scenarios
Xu, J. C., & Howard, A. M. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525669 · Cited by 46 · see citing works
You, S., & Robert, L. (2019) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2019.031 · Cited by 37 · see citing works
Self-citationThe Ultimatum Game as Measurement Tool for Anthropomorphism in Human–Robot Interaction
Torta, E., van Dijk, E., Ruijten, P. A. M., & Cuijpers, R. H. (2013) · Lecture notes in computer science · doi:10.1007/978-3-319-02675-6_21 · Cited by 34 · see citing works
Human Trust After Robot Mistakes: Study of the Effects of Different Forms of Robot Communication
Ye, S., Neville, G., Schrum, M., Gombolay, M., Chernova, S., & Howard, A. M. (2019) · IEEE RO-MAN · doi:10.1109/ro-man46459.2019.8956424 · Cited by 31 · see citing works
Esterwood, C. T., & Robert, L. (2021) · ROBONOMICS The Journal of the Automated Economy · doi:10.2139/ssrn.3767609 · Cited by 23 · see citing works
Self-citationAre you satisfied yet? Shared leadership, trust and individual satisfaction in virtual teams
Robert, L., & You, S. (2013) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign) · doi:10.9776/13255 · Cited by 21 · see citing works
Self-citationHuman Perceptions of the Severity of Domestic Robot Errors
Rossi, A., Dautenhahn, K., Koay, K. L., & Walters, M. L. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70022-9_64 · Cited by 21 · see citing works
A Meta-Analysis of Human Personality and Robot Acceptance in Human-Robot InteractionPreprint
Esterwood, C. T., Essenmacher, K., Yang, H., Zeng, F., & Robert, L. (2021) · SSRN Electronic Journal · doi:10.2139/ssrn.3768022 · Cited by 17 · see citing works
Self-citationRobot's Impression of Appearance and Their Trustworthy and Emotion Richness
Matsui, T., & Yamada, S. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525796 · Cited by 15 · see citing works
The Role of Behavioral Anthropomorphism in Human-Automation Trust Calibration
Jensen, T., Khan, M. M. H., & Albayram, Y. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-50334-5_3 · Cited by 12 · see citing works
Meta-analysis of the Effect of Imperfect Alert Automation on System Performance
Rein, J. R., Masalonis, A., Messina, J., & Willems, B. (2013) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1541931213571062 · Cited by 11 · see citing works
Costa, A. C., Ferrin, D. L., & Fulmer, C. A. (2017) · SAGE Publications Ltd eBooks · doi:10.4135/9781473914957.n20 · Cited by 10 · see citing works
Exploring the Efficacy of Social Trust Repair in Human-Automation Interactions
Quinn, D. B. (2018) · TigerPrints (Clemson University) · Cited by 6 · see citing works
Personality in Healthcare Human Robot Interaction (H-HRI): A Literature Review and Brief CritiquePreprint
Esterwood, C. T., & Robert, L. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2008.06723 · Cited by 6 · see citing works
Self-citation
Flagged first, then by citations
5 of the 53 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Wischnewski, M., Krämer, N., & Müller, E. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581197 · Cited by 146 · see citing works
“However, Esterwood and Robert [34] could show that promising no errors in the future as well as explanations for why the error happened showed greater efects than mere apologies (see also results by [136]).”
BackgroundInfluentialHuman-AI interaction research agenda: A user-centered perspective
Tingting, J., Sun, Z., Fu, S., & Lv, Y. (2024) · Data and Information Management · doi:10.1016/j.dim.2024.100078 · Cited by 146 · see citing works
Esterwood, C., & Jr, L. P. R. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107658 · Cited by 98 · see citing works
“…of robots (anthropomorphic vs. machine-like), tasks, behaviors, and settings might impact the relationship between trust repair and trustworthiness in HRI especially given findings from other research highlighting how some of these may be influential to trust resilience [83] and trust repair [22].”
BackgroundResultSelf-citationExplanations and trust: What happens to trust when a robot partner does something unexpected?
Lyons, J. B., Hamdan, I. A., & Vo, T. Q. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107473 · Cited by 97 · see citing works
Ethics in human–AI teaming: principles and perspectives
Pflanzer, M., Traylor, Z., Lyons, J. B., Dubljević, V., & Nam, C. S. (2022) · AI and Ethics · doi:10.1007/s43681-022-00214-z · Cited by 106 · see citing works
The Effects of Explanations in Automated Essay Scoring Systems on Student Trust and Motivation
Conijn, R., Kahr, P. K., & Snijders, C. C. P. (2023) · Journal of Learning Analytics · doi:10.18608/jla.2023.7801 · Cited by 100 · see citing works
“Additionally, research has studied ways to communicate after a system failure, for instance, by using different repair strategies, apologies, promises, justifications, or explanations with different attributions (cf. Kim & Song, 2021; Esterwood & Robert, 2021; Matzat & Snijders, 2012).”
BackgroundA Literature Review of Trust Repair in HRI
Esterwood, C. T., & Robert, L. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900667 · Cited by 61 · see citing works
“Trust, or accepting the vulnerability associated with relying on others, is vital for human–robot teams but is often decreased by trust violations [16], [17], [18].”
BackgroundSelf-citationRoesler, E., Vollmann, M., Manzey, D., & Onnasch, L. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.108008 · Cited by 62 · see citing works
The theory of mind and human–robot trust repair
Esterwood, C. T., & Robert, L. (2023) · Scientific Reports · doi:10.1038/s41598-023-37032-0 · Cited by 58 · see citing works
Self-citationKahr, P. K., Rooks, G., Willemsen, M. C., & Snijders, C. C. P. (2024) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3686164 · Cited by 49 · see citing works
“The current research landscape on trust in AI covers several aspects: the influence of system properties [30, 85, 92], user traits [6, 18, 61], the interaction modalities between the user and an AI system [9, 21, 28], or the task context of human-AI-decision-making [88].”
Understanding the Evolvement of Trust Over Time within Human-AI Teams
Duan, W., Zhou, S., Scalia, M. J., Yin, X., Weng, N., Zhang, R., Freeman, G., McNeese, N. J., Gorman, J. C., & Tolston, M. T. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3687060 · Cited by 53 · see citing works
“…of AI trust calibration strategies have focused on AI’s reliability [106] or do not differentiate these sub-constructs [106], despite a few exceptions (e.g., [32]) Our work has demonstrated that besides AI’s reliability, its integrity, benevolence, and adaptability aspects also warrant calibration.”
Kahr, P. K., Rooks, G., Willemsen, M. C., & Snijders, C. C. P. (2023) · ACM International Conference on Intelligent User Interfaces (IUI) · doi:10.1145/3581641.3584058 · Cited by 43 · see citing works
“XAI can be in textual or visual form, to complement a recommendation (e.g. counterfactual examples, probability values), as interventions (warnings, nudges), or post-decision arguments (apologies, promises, justifications) [8, 9, 22, 23, 37, 40, 55, 63, 68].”
BackgroundHaving the Right Attitude: How Attitude Impacts Trust Repair in Human—Robot Interaction
Esterwood, C., & Robert, L. P. (2022) · 2022 17th ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri53351.2022.9889535 · Cited by 36 · see citing works
Self-citationTrust and AI weight: human-AI collaboration in organizational management decision-making
Wen, Y., Wang, J., & Chen, X. (2025) · Frontiers in Organizational Psychology · doi:10.3389/forgp.2025.1419403 · Cited by 47 · see citing works
Heterogeneous human–robot task allocation based on artificial trust
Ali, A., Azevedo-Sa, H., Tilbury, D. M., & Robert, L. (2022) · Scientific Reports · doi:10.1038/s41598-022-19140-5 · Cited by 32 · see citing works
“Miscalibrated trust can result in suboptimal outcomes, motivating the development of trust-aware robots that can modify their behavior to manipulate or repair humans’ trust 53–55 .”
BackgroundSelf-citationZhang, Y., Li, Z., Guo, H., Wang, L., Chen, Q., Jiang, W., Fan, M., Zhou, G., & Gong, J. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3580884 · Cited by 29 · see citing works
“The machine form of interactive and assistive robots has an essential impact on the efciency and experience of humanrobot collaboration, such as trust [1, 23], companionship [11], and communication [33, 46].”
BackgroundThe Robot That Showed Remorse: Repairing Trust with a Genuine Apology
Pompe, B. L., Velner, E., & Truong, K. P. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900860 · Cited by 27 · see citing works
“Several different trust repair strategies have been investigated in previous research [22][5][23][24][4][25] with mostly positive effects.”
BackgroundKarli, U. B., Cao, S., & Huang, C. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568162.3576964 · Cited by 26 · see citing works
“…one online study using a simulated task showed that, among the four trust repair strategies mentioned, promise was most effective at repairing integrity and benevolence aspects of trust in non-anthropomorphic robot, while explanation was most effective at repairing ability aspects of trust [14].”
BackgroundResultInfluentialWhen Do People Want an Explanation from a Robot?
Wachowiak, L., Fenn, A., Kamran, H., Coles, A., Çeliktutan, O., & Canal, G. (2024) · IEEE/ACM International Conference on Human-Robot Interaction · doi:10.1145/3610977.3634990 · Cited by 43 · see citing works
“In another study [9], explanations of robot errors are compared with other strategies, namely apologizing, promising improvement, and denial.”
ResultEvaluating the Impact of Emotional Apology on Human-Robot Trust
Xu, J. C., & Howard, A. (2022) · IEEE RO-MAN · doi:10.1109/ro-man53752.2022.9900518 · Cited by 26 · see citing works
“Although there have been studies focusing on trust repair in different scenarios [4-7], few were concentrating on developing trust repair between human and robot in a driving scenario.”
BackgroundResultInfluentialWang, B., Yuan, T., & Rau, P. P. (2024) · International Journal of Social Robotics · doi:10.1007/s12369-024-01132-2 · Cited by 21 · see citing works
The Trust Recovery Journey. The Effect of Timing of Errors on the Willingness to Follow AI Advice.
Kahr, P. K., Rooks, G., Snijders, C., & Willemsen, M. C. (2024) · Proceedings of the 29th International Conference on Intelligent User Interfaces · doi:10.1145/3640543.3645167 · Cited by 21 · see citing works
AI apology: a critical review of apology in AI systems
Harland, H., Dazeley, R., Senaratne, H., Vamplew, P., Cruz, F., & Nakisa, B. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11305-8 · Cited by 19 · see citing works
Kraus, J., Merger, J., Gröner, F., & Pätz, J. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568294.3580122 · Cited by 19 · see citing works
“Trust repair strategies can be defined as ‘ efforts undertaken to restore trust following an actual or perceived trust violation ’ [13, p. 1].”
BackgroundRogers, K., Webber, R. J. A., & Howard, A. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568294.3580178 · Cited by 17 · see citing works
“Prior work has shown that the best way to repair trust after such competency violations is through explanations [5] and apologies [17].”
BackgroundPeople Dynamically Update Trust When Interactively Teaching Robots
Chi, V. B., & Malle, B. F. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568162.3576962 · Cited by 17 · see citing works
“Some researchers have adapted scales developed for human-human relations [55] to the human-robot case (e.g., [5, 27]), though not always with independent validation.”
BackgroundA theoretical model to explain mixed effects of trust repair strategies in autonomous systems
Pak, R., & Rovira, E. M. (2023) · Theoretical Issues in Ergonomics Science · doi:10.1080/1463922x.2023.2250424 · Cited by 16 · see citing works
Virtually the Same or Realistically Different?: A Meta-Analysis of Real vs. ‘Not So Real’ Robots
Esterwood, C. T., Guan, R., Ye, X., & Robert, L. (2025) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1109/hri61500.2025.10974086 · Cited by 15 · see citing works
Self-citationThe Emotional Dilemma: Influence of a Human-like Robot on Trust and Cooperation
Becker, D., Rueda, D., Beese, F., Torres, B. S. G., Lafdili, M., Ahrens, K., Fu, D., Strahl, E., Weber, T., & Wermter, S. (2023) · IEEE RO-MAN · doi:10.1109/ro-man57019.2023.10309321 · Cited by 13 · see citing works
“However, the statement is deliberately left open as promises have shown a strong effect on trust repair [64], [65].”
BackgroundKahr, P. K., Rooks, G., Snijders, C. C. P., & Willemsen, M. (2025) · International Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713099 · Cited by 19 · see citing works
Axelsson, M., Spitale, M., & Güneş, H. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610977.3634948 · Cited by 11 · see citing works
The Effect of Voice and Repair Strategy on Trust Formation and Repair in Human-Robot Interaction
Romeo, M., Torre, I., Maguer, S. L., Sleat, A., Cangelosi, A., & Leite, I. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3711938 · Cited by 11 · see citing works
Spitale, M., Axelsson, M., Kara, N., & Güneş, H. (2023) · IEEE RO-MAN · doi:10.1109/ro-man57019.2023.10309386 · Cited by 9 · see citing works
“Esterwood and Robert also examined how a robot’s different trust repair strategies changed people’s trust in a robot when it made mistakes during a box sorting task [36], [37], finding that explanations were the most effective strategy for repairing trust after multiple violations.”
BackgroundEffects of Explanations by Robots on Trust Repair in Human-Robot Collaborations
Bai, Z., & Chen, K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-60611-3_1 · Cited by 8 · see citing works
Human-Computer Interaction in Intelligent Environments
Stephanidis, C., & Salvendy, G. (2024) · CRC Press eBooks · doi:10.1201/9781003490685 · Cited by 8 · see citing works
Ayub, A., Francesco, Z. D., Holthaus, P., Nehaniv, C. L., & Dautenhahn, K. (2025) · International Journal of Social Robotics · doi:10.1007/s12369-025-01214-9 · Cited by 12 · see citing works
“Most prior research showed that people lose trust in autonomous robots that might be imperfect.”
Cantucci, F., Marini, M., & Falcone, R. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3786770 · Cited by 11 · see citing works
“Observing the robot performance represents the “good rational reasons” for building cognitive trust in robots [21].”
Robot Broken Promise? Repair strategies for mitigating loss of trust for repeated failures
Nesset, B., Romeo, M., Rajendran, G., & Hastie, H. (2023) · IEEE RO-MAN · doi:10.1109/ro-man57019.2023.10309558 · Cited by 8 · see citing works
“As discussed by Esterwood and Robert [19], the human-human literature has classified these efforts as either apologies, denials, explanations, or promises.”
BackgroundLie, Repent, Repeat: Exploring Apologies after Repeated Robot Deception
Rogers, K., Webber, R. J. A., Chang, J., Zubizarreta, G. G., & Howard, A. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610977.3634980 · Cited by 7 · see citing works
“Therefore, for machines, a more applicable defni-tion, as suggested by Masters et al. [17], focuses on the receiver’s perception, categorizing behavior as deceptive if has “the potential to mislead" irrespective of the machine’s internal mechanisms or intentions.”
MethodA Validation of the Human-Generative Artificial Intelligence Trust Scale
Wang, P., Yin, K., Tian, M., Zheng, Y., Wu, H., Zhou, C. Q., Zhang, M., Ma, J., & Yuan, X. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2542881 · Cited by 7 · see citing works
“As a prerequisite for promoting GAI technology and human-AI collaboration (Dwivedi et al., 2024; Esterwood & Robert, 2021; You & Robert, 2018), human-AI trust, and even trust between humans and GAI, has become a significant topic within the field of human – AI interaction research.”
ERR@HRI 2.0 Challenge: Multimodal Detection of Errors and Failures in Human-Robot Conversations
Cao, S., Stiber, M., Mahmood, A., Parreira, M. T., Ju, W. G., Spitale, M., Güneş, H., & Huang, C. (2025) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3746027.3762073 · Cited by 5 · see citing works
“Previous works have discovered some strategies to help robots rebuild user trust and recover from errors [8, 14]; however, the application of these strategies hinges on the robot’s ability to detect its own errors effectively [30].”
Systematic methods for Moral HRI: Studying human responses to robot norm conflictsPreprint
Phillips, E. J., Malle, B. F., Rosero, A., Kim, M. J., Kim, B., Melles, L., & Chi, V. B. (2023) · doi:10.31234/osf.io/by4rh · Cited by 5 · see citing works
Schelble, B. G., Textor, C., Zhang, R., Lopez, J., Taverez, N., Ku, C., McNeese, N. J., Pak, R., Freeman, G., Tossell, C. C., et al. (2025) · Ergonomics · doi:10.1080/00140139.2025.2570300 · Cited by 5 · see citing works
“…work points out that when unethical behaviours occur, these trust repair strategies (e.g. apology, denial) may be employed to restore trust (esterwood and Robert 2021). however, current work on trust repair strategies has shown inconsistent findings across various team interactions, and…”
The Warehouse Robot Interaction Sim
Esterwood, C. T., & Robert, L. (2023) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3568294.3580086 · Cited by 4 · see citing works
“Results of this showed that anthropomorphism appeared to play some role in the efficacy of different repairs [8].”
BackgroundMethodInfluentialSelf-citationInteractive Human-Robot Teaching Recovers and Builds Trust, Even With Imperfect Learners
Chi, V. B., & Malle, B. F. (2024) · ACM/IEEE International Conference on Human-Robot Interaction (HRI) · doi:10.1145/3610977.3634977 · Cited by 3 · see citing works
“However, trust has typically been treated as a response to machine reliability and competence [12, 52, 55, 58], whereas the domain of social interaction and the learning of social norms introduces a richer set of feelings and judgments of trust [25, 38, 43, 69].”
BackgroundLane, S., Esterwood, C. T., Kulić, D., & Robinson, N. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731465 · Cited by 3 · see citing works
“Current trust repair research has centred on the effects that four distinct verbal repair strategies (apologies, explanations, promises, and denial) each have on the three sub-dimensions of trust (competence, integrity, and benevolence) [10], [11], [12].”
BackgroundInfluentialSelf-citationGoo, H. G., Schadenberg, B. R., Kolkmeier, J., Truong, K. P., & Evers, V. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719739 · Cited by 3 · see citing works
“Often examining apologies [7, 12, 19, 29, 33], denials [12, 33], explanations [7, 12, 25], and promises [12, 29].”
Nagy, T.-N., Khavas, Z. R., Kotturu, M. R., Liefooghe, B., Robinette, P., & de Graaf, M. M. A. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3767729 · Cited by 3 · see citing works
“…robot’s performance” [21] (p.1), and have been induced in empirical research through speech recognition errors or unintentional mistakes leading to unsatisfactory functioning, such as the robot dropping an egg, delivering the wrong drink or giving the human the wrong box [18, 20, 22, 31, 38, 54].”
Promises and Trust Repair in UGVs
Esterwood, C. T., Ali, A., George, Z., Dubrow, S. R., Smereka, J. M., Riegner, K. L., Tilbury, D. M., & Robert, L. (2023) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/21695067231196235 · Cited by 3 · see citing works
“This study seeks to build upon previous work conducted on trust repair in HRI (Esterwood & Robert, 2021, 2023) by examining the specific trust repair strategy of promises in isolation while also focusing on the context area of UGVs as opposed to robots in general.”
MethodResultSelf-citationTurriziani, L., Kraus, J., Ruess, S., Zeng, Z., & Kannan, S. S. (2024) · IEEE RO-MAN · doi:10.1109/ro-man60168.2024.10731231 · Cited by 3 · see citing works
“This proposition is for example based on the research on the influence of errors and unreliability on trust [38] and robotic trust repair strategies aiming to gain back a person’s trust after error through apologies, denials, explanations or promises [39].”
BackgroundZafari, S., Papagni, G., & Puthenkalam, J. (2025) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3765766.3769422 · Cited by 2 · see citing works
“Still, most studies remain robot-centric, emphasizing corrective actions taken by robots rather than users’ role in failure resolution [5, 6, 29], leaving a gap in understanding how human agency interacts with robot communication.”
Conijn, R., Kahr, P. K., & Snijders, C. C. P. (2023) · doi:10.31234/osf.io/tgpf4 · Cited by 2 · see citing works
The Effects of Framing and Apology in HRI in Manufacturing
Pietras, E., Ulfert, A., Borzone, G., Kramberger, A., Krüger, N., & Bodenhagen, L. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711703 · Cited by 1 · see citing works
“Differences in people’s attitudes towards anthropomorphic robots compared with collaborative robotic arms may also play a role in the effects of trust repair, as well as differences in attitudes towards real and virtual robots [9].”
BackgroundThe Last Mile in Remote Sensing Poverty Prediction
Jung, W.-J., Sinha, A., Kim, A. H., Shah, V. N., Lu, Y., Lee, L., & Ammari, T. (2025) · ACM Journal on Computing and Sustainable Societies · doi:10.1145/3724422 · Cited by 1 · see citing works
Would You Trust Me Now? A Study on Trust Repair Strategies in Human-Robot Collaboration
Mélot-Chesnel, J., & de Graaf, M. M. A. (2025) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra55743.2025.11127698 · Cited by 1 · see citing works
Granting a Second Chance: How Recovery Strategies Shape Perceptions of Intelligent Agent Errors
Miao, X., Tang, J., Peng, K., & Wang, F. (2025) · IEEE RO-MAN · doi:10.1109/ro-man63969.2025.11217814 · Cited by 2 · see citing works
“Recovery strategies play a vital role in re-establishing trust and maintaining user engagement [7].”
Robots with Heart: How Emotion Regulation Shapes Human-Robot Interaction
Jia, L., Ayoub, J., Tian, D., Song, M., Moradi‐Pari, E., & Du, N. (2026) · International Journal of Social Robotics · doi:10.1007/s12369-026-01358-2 · Cited by 1 · see citing works
Wang, C., & Liu, X. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2707710 · Cited by 1 · see citing works
Filthaut, L., Murray-Rust, D., Lupetti, M. L., Lii, N. Y., Schmaus, P., & Leidner, D. S. (2024) · International Conference on Space Robotics (iSpaRo) · doi:10.1109/isparo60631.2024.10687823 · Cited by 1 · see citing works
“…during error-free and erroneous situations [20], design principles for safety in HRI [21], comparisons of error-handling strategies in human-human and human-robot dialogues [22], approaches for repairing trust after robot errors [23], and investigation into human-robot trust repair strategies [24].”
BackgroundError recovery in wearable robotic Co-Grasping: the role of human-led correction
Chang, E. Y., Torres, W. O., & Stuart, H. S. (2025) · Frontiers in Robotics and AI · doi:10.3389/frobt.2025.1598296 · Cited by 1 · see citing works
de Diesbach, P. B. L., Bagozzi, R. P., & Jean-Philippe, A. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4153238
Broken Trust: Does the Agent Matter?
Nesset, B., Rajendran, G., & Romeo, M. (2024) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3687272.3688307
“As highlighted by Esterwood and Robert [13], Human-Human interaction literature has already classified these efforts as either apologies, denials, explanations or promises [25, 28].”
What Should Autonomous Robots Verbalize and What Should They Not?
Yoshihara, D., Yuguchi, A., Kawano, S., Iio, T., & Yoshino, K. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-2074-6_2
Online Continual Learning for Automatic Error Prevention in Data-Driven HRI
Ravishankar, J., Doering, M., & Kanda, T. (2025) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3759160
Mortal vs. Machine: A Compact Two-Factor Model for Comparing Trust in Humans and Robots
Prahl, A. (2025) · Robotics · doi:10.3390/robotics14080112
Factors Influencing Trust in Human-Robot Interaction: A Case Study
Cocato, L., Erlhagen, W., Bicho, E. G., Vicente, P., & Ferreira, F. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-97589-9_28
Elangovan, G., Shanmugam, S., Bharti, P., Sharma, V., & Gupta, R. (2025) · doi:10.1109/icrai68431.2025.11396701
“Anthropomorphism positively impacts users’ trust while being used as a trust repair strategy [16].”
Enhancing Trust Repair in Driving: The Role of AI Emotional Expression and Perceived Control
Hu, X., Dees, L., Gopinath, D., Silva, A., DeCastro, J., Rosman, G., & Sumner, E. S. (2025) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc60802.2025.11423450
“Trust is a critical component of human-machine teaming, defined as a willingness to be vulnerable to another entity based on positive expectations of its intentions or behavior [8], [21], [32].”
“Let Me Try Again”—Leveraging Social Interfaces to Improve Trust in Automation for Teleoperation
Pournasir, P., & Rea, D. J. (2026) · ACM Transactions on Human-Robot Interaction · doi:10.1145/3819073
“Two important social trust repair strategies are acknowledging mistakes [25, 53] and promising to do better in the future [15].”
Guerra, E., Semmelhack, E. A., Schacht, A., & Prilla, M. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3820271
“Researchers frequently rely on simulations of robots to carry out user studies with robots [10, 11, 23, 32].”
Guerra, E., Söhngen, Y. N., & Prilla, M. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3820273
“HCI and HRI researchers frequently rely on simulations of robots to carry out user studies [12, 13, 19, 34].”
The Impact of a Third-Party on Trust of a Human-Robot Dyad
Söhngen, Y. N., Sepp, A., & Prilla, M. (2026) · Mensch und Computer (MuC) · doi:10.1145/3820253.3820278
“Astrustisa multidimensional construct [38] and trust repair strategies work better for specific dimensions than others [16, 42], it might be that the third-party influence also works better for repairing specific trust violations.”
Explanation Transparency and Trust Perception After Generative AI Failure: A Dual-Path Model
Yang, Q., & Dong, H. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2734323
Rabe, J., Sciutti, A., Zonca, J., Pasquali, D., & Rea, F. (2026) · Springer tracts in advanced robotics · doi:10.1007/978-3-032-19374-2_22
Trust Disruption from LLM Hallucinations in Embodied vs. Text-Based Agents
Sheng, X., & Kim, S. (2026) · IEEE RO-MAN · doi:10.1109/ro-man72286.2026.11714933
“When such errors lead to interaction failures, they can violate users’ expectations and reduce perceived trustworthiness [18], [14].”
- Authors
- Esterwood, C., & Robert, L. P.
- Affiliations
- University of Michigan (US)
- Venue
- 2021 30th IEEE International Conference on Robot & Human Interactive Communication (RO-MAN)
- Publisher
- IEEE
- Published
- 2021-08-08
- Pages
- 183-188
- ISSN
- 1944-9437 (ISSN-L)
- OpenAlex
- W3184347428
- Cited by
- 75
- References
- 53
- Citations by year
- 2026: 9 · 2025: 22 · 2024: 17 · 2023: 19 · 2022: 8
- FWCI
- 18.76
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Locations
- IEEE RO-MAN (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Funders
- University of Michigan
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- trust in robots, anthropomorphism, trustworthiness, ability, benevolence, integrity, human-robot collaboration
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
