On the impact of different types of errors on trust in human-robot interaction: Are laboratory-based HRI experiments trustworthy?

Flook, Rebecca; Shrinah, Anas; Wijnen, Luc; Eder, Kerstin; Melhuish, Chris; Lemaignan, Séverin

doi:10.1075/is.18067.flo

Article published In:

Social Cues in Robot Interaction, Trust and Acceptance
Edited by Alessandra Rossi, Kheng Lee Koay, Silvia Moros, Patrick Holthaus and Marcus Scheunemann
[Interaction Studies 20:3] 2019
► pp. 455–486

On the impact of different types of errors on trust in human-robot interaction

Are laboratory-based HRI experiments trustworthy?

Rebecca Flook | University of Bristol

Anas Shrinah | University of Bristol

Luc Wijnen | Radboud University

Kerstin Eder | University of Bristol

Chris Melhuish | University of the West of England

Séverin Lemaignan | University of the West of England

Trust is a key dimension of human-robot interaction (HRI), and has often been studied in the HRI community. A common challenge arises from the difficulty of assessing trust levels in ecologically invalid environments: we present in this paper two independent laboratory studies, totalling 160 participants, where we investigate the impact of different types of errors on resulting trust, using both behavioural and subjective measures of trust. While we found a (weak) general effect of errors on reported and observed level of trust, no significant differences between the type of errors were found in either of our studies. We discuss this negative result in light of our experimental protocols, and argue for the community to move towards alternative methodologies to assess trust.

Keywords: human-robot interaction, trust

Article outline

1.Introduction
- 1.1Factors affecting trust
  - 1.1.1Robot factors
    - Robot errors
    - Etiquette
    - Communication style
    - Behaviour transparency
  - 1.1.2Human factors
    - Human perceptions of robots
    - Previous experience of robots
    - Personality traits
  - 1.1.3Environmental factors
    - Severity of human-robot interaction scenario
- 1.2Measuring trust in human-robot interaction
  - Questionnaires
- 1.3Investigating the impact of different errors on trust
  - 1.3.1Research questions
  - 1.3.2Hypotheses
2.Study 1: Impact of errors and robot acknowledgement of errors on trust
- 2.1Methodology
  - 2.1.1Experimental procedure
    - Robot control
    - Procedure
  - 2.1.2Data collection
  - 2.1.3Participants demographics
- 2.2Results
  - 2.2.1Hypothesis 1: No error vs. erroneous conditions
  - 2.2.2Hypothesis 2: Technical failure vs. decision-level error conditions
  - 2.2.3Hypothesis 3: Acknowledgement vs. no acknowledgement when a fault occurs
  - 2.2.4Errors and acknowledgement behaviours conditions internal interactions
3.Study 2: Impact of errors on proxemics
- 3.1Methodology
  - 3.1.1Experiment procedure
  - 3.1.2Data collection
  - 3.1.3Participant demographics
- 3.2Results
  - 3.2.1Faulty behaviour vs baseline
  - 3.2.2Technical vs socio-cognitive error
4.Discussion
- 4.1Potential confounds
- 4.2A lack of negative results?
5.Conclusion
6.Resources for replication
- Study
- Data analysis
Acknowledgements
Note
References

Published online: 18 November 2019

https://doi.org/10.1075/is.18067.flo

References (44)

References

Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., & Kievit, R. (2018, August). Raincloud plots: a multi-platform tool for robust data visualization. PeerJ Preprints, 6, e27137v1. Retrieved from

Barber, B. (1983). The logic and limits of trust.

Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009). Measurement instruments for the anthropomorphism, animacy, likeability, perceived intelligence, and perceived safety of robots. International journal of social robotics, 1 (1), 71–81.

Bartneck, C., Suzuki, T., Kanda, T., & Nomura, T. (2007). The influence of people’s culture and prior experiences with aibo on their attitude towards robots. Ai & Society, 21 (1–2), 217–230.

Baxter, P., Kennedy, J. E. S., Lemaignan, S., & Belpaeme, T. (2016). From characterising three years of hri to methodology and reporting recommendations. In Proceedings of the 2016 acm/ieee human-robot interaction conference (alt.hri).

Bickmore, T., Pfeifer, L., Schulman, D., Perera, S., Senanayake, C., & Nazmi, I. (2008). Public displays of affect: Deploying relational agents in public spaces. In Proceedings of chi’08 (pp. 3297–3302).

Breazeal, C., Kidd, C., Thomaz, A., Hoffman, G., & Berlin, M. (2005). Effects of nonverbal communication on efficiency and robustness in human-robot teamwork. In Proceedings of the ieee international conference on intelligent robots and systems (pp. 708–713).

Corritore, C., Kracher, B., & Wiedenbeck, S. (2003). Online trust: Concepts, evolving themes, a model. International Journal of Human-Computer Studies, 58(6), 737–758.

Dautenhahn, K., Woods, S., Kaouri, C., Walters, M., Koay, K., & Werry, I. (2005). What is a robot companion – friend, assistant or butler. In Proceedings of the ieee international conference on intelligent systems and robots (pp. 1192–1197).

Desai, M., Medvedev, M., Vázquez, M., McSheehy, S., Gadea-Omelchenko Bruggeman, S., … Yanco, H. (2012). Effects of changing reliability on trust of robot systems. In Proceedings of the acm/ieee conference on human-robot interaction (pp. 73–80).

Goetz, J., Kiesler, S., & Powers, A. (2003). Matching robot appearance and behaviour to tasks to improve human-robot interaction. In Proceedings of ieee roman international workshop on robot and human interactive communication (pp. 55–60).

Gosling, S. D., Rentfrow, P. J., & Swann, W. B. (2003). A very brief measure of the big five personality domains. Journal of Research in Personality, 371, 504–528.

Gray, K., & Wegner, D. (2012). Feeling robots and human zombies: Mind perception and the uncanny valley. Cognition, 125(1), 125–130.

Guznov, S., Lyons, J., Nelson, A., & Woolley, M. (2016). The effects of automation error types on operators’ trust and reliance. In S. Lackey & R. Shumaker (Eds.), Virtual, augmented and mixed reality (pp. 116–124). Cham: Springer International Publishing.

Hamacher, A., Bianchi-Berthouze, N., Pipe, A. G., & Eder, K. (2016). Believing in BERT: Using expressive communication to enhance trust and counteract operational error in physical human-robot interaction. In Robot and human interactive communication (ro-man), 2016 25th ieee international symposium on (pp. 493–500).

Hancock, P., Billings, D., Schaefer, K., Chen, J., de Visser, E., & Parasuraman, R. (2011). A meta-analysis of factors affecting trust in human-robot interaction. Human Factors, 53(5), 517–527.

Iwamura, Y., Shiomi, M., Kanda, T., Ishiguro, H., & Hagita, N. (2011). Do elderly people prefer a conversational humanoid as a shopping assistant partner in supermarkets. In Proceedings of the acm/ieee international conference on human-robot interaction (pp. 449–456).

Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human factors, 46(1), 50–80.

Lee, J. J., Knox, W. B., Wormwood, J. B., Breazeal, C., & Desteno, D. (2013). Computationally modelling interpersonal trust. Frontiers in Psychology, 4(893).

Lee, M., Kiesler, S., & Forlizzi, J. (2010). Receptionist or information kiosk: how do people talk with a robot? In Proceedings of the 2010 acm conference on computer supported cooperative work (pp. 31–40).

Lemaignan, S., Fink, J., & Dillenbourg, P. (2014). The dynamics of anthropomorphism in robotics. In in proceedings of the international conference on human-robot interaction (pp. 226–227).

Lucas, G., Boberg, J., Traum, D., Artstein, R., Gratch, J., Gainer, A., … Leuski, A. (2018). Getting to know each other: The role of social dialogue in recovery from errors in social robots. In Proceedings of the 2018 acm/ieee international conference on human-robot interaction (pp. 344–351).

Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709–734.

Mirnig, N., Stollnberger, G., Miksch, M., Stadler, S., Giuliani, M., & Tscheligi, M. (2017). To err is robot: How humans assess and act toward an erroneous social robot. In Frontiers in robotics and ai.

Moray, N., & Inagaki, T. (1999). Laboratory studies of trust between humans and machines in automated systems. Transactions of the Institute of Measurement and Control, 21 (4–5), 203–211.

Mori, M. (1970). The uncanny valley. Energy, 7(4), 33–35.

Muir, B., & Moray, N. (1996). Trust in automation: Part II. “experimental studies of trust and human intervention in a process control simulation.”. In Ergonomics (pp. 429–460).

Muir, B. M. (1994). Trust in automation: Part i. theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37(11), 1905–1922.

Nass, C., & Lee, K. (2000). Does computer-generated speech manifest personality? an experimental test of similarity-attraction. In Proceedings of chi’00 (p. 329–336).

Nomura, T., & Kanda, T. (2003, Nov). On proposing the concept of robot anxiety and considering measurement of it. In The 12th ieee international workshop on robot and human interactive communication, 2003. proceedings. roman 2003. (p. 373–378).

Pages, J., Marchionni, L., & Ferro, F. (2016). Tiago: the modular robot that adapts to different research needs. In International workshop on robot modularity, iros.

Parasuraman, R., & Miller, C. (2004). Trust and etiquette in high-criticality automated systems. Communication of the ACM, 47(4), 51–55.

Ray, C., Mondada, F., & Siegwart, R. (2008). What do people expect from robots? In Proceedings of the ieee/rsj 2008 international conference on intelligent robots and systems (pp. 3816–3821).

Robinette, P., Howard, A. M., & Wagner, A. R. (2017). Effect of robot performance on human-robot trust in time-critical situations. IEEE Transactions on Human-Machine Systems, 47(4), 425–436.

Robinette, P., Li, W., Allen, R., Howard, A. M., & Wagner, A. R. (2016). Overtrust of robots in emergency evacuation scenarios. In The eleventh acm/ieee international conference on human robot interaction (pp. 101–108).

Salem, M., Eyssel, F., Rohlfing, K., Kopp, S., & Joulbin, F. (2013). To err is human(-like): Effects of robot gesture on perceived anthropomorphism and likeability. International Journal of Social Robotics, 51, 313–323.

Salem, M., Lakatos, G., Amirabdollahian, F., & Dautenhahn, K. (2015). Would you trust a (faulty) robot?: Effects of error, task type and personality on human-robot cooperation and trust. In Proceedings of the tenth annual acm/ieee international conference on human-robot interaction (pp. 141–148).

Sarkar, S., Araiza-Illan, D., & Eder, K. (2017). Effects of faults, experience, and personality on trust in a robot co-worker. arXiv preprint arXiv:1703.02335.

Shiomi, M., Kanda, T., Ishiguro, H., & Hagita, N. (2006). Interactive humanoid robots for a science museum. In Proceedings of the 1st acm sigchi/sigart conference on human-robot interaction (pp. 305–312).

Sidner, C., Lee, C., & Lesh, N. (2003). Engagement rules for human-robot collaborative interactions. In Proceedings of the ieee international conference on systems man and cybernetics (pp. 3957–3962).

Thrun, S., Schulte, J., & Rosenburg, C. (2000). Interaction with mobile robots in public places. IEEE Intelligent Systems, 7–11.

Wiegmann, D. A., Rich, A., & Zhang, H. (2001). Automated diagnostic aids: The effects of aid reliability on users’ trust and reliance. In Theoretical issues in ergonomic science (p. 352–367).

Wilson, J., Straus, S., & McEvily, B. (2006). All in due time: The development of trust in computer-mediated and face-to-face teams. Organizational Behaviour and Human Decision Processes, 99(1), 16–33.

Wortham, Robert H., and Andreas Theodorou. (2017). “Robot transparency, trust and utility.” Connection Science 29.3 (2017): 242–248.

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