Abstract
Eyes are an important organ in both information processing and communicating. Eye gaze contains rich information about our attention and internal state. It has previously been studied as a measure of trust in various contexts such as air traffic control, driving, and online shopping. The study uses fixation and saccadic measurements to obtain a viable measure of human trust. The experiment involved nine participants and their trust level was indicated by whether they accepted or rejected a decision made by an Artificial Intelligence (AI). A Tobii pro nano eye-tracker and psychopy software were used to track the participants’ eye gaze and responses. Results indicate that saccade count shows a statistically significant variation between trust and mistrust conditions with p < 0.05, while the fixation count showed a variation at p < 0.2. Through this study, we show that the count of saccades is a viable measure of a human’s mistrust in an AI system.
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References
Prinet, J.C., Sarter, N.B.: The effects of high stress on attention: a first step toward triggering attentional narrowing in controlled environments. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 59(1), pp. 1530–1534 (2015). https://doi.org/10.1177/1541931215591331
Geitner, C., et al.: A link between trust in technology and glance allocation in on-road driving. In: Driving Assessment Conference, vol. 9 (2017). https://doi.org/10.17077/drivingassessment.1645
Hergeth, S., Lorenz, L., Vilimek, R., Krems, J.F.: Keep your scanners peeled: gaze behavior as a measure of automation trust during highly automated driving. Hum Factors. 58(3), 509–519 (2016). https://doi.org/10.1177/0018720815625744. Epub 2016 Feb 3 PMID: 26843570
Jacob, R.J., Karn, K.S.: Eye tracking in human-computer interaction and usability research: Ready to deliver the promises. In: The mind’s eye, North-Holland, pp. 573–605 (2003)
Freeman, J.B.: Doing psychological science by hand. Curr. Dir. Psychol. Sci. 27(5), 315–323 (2018). https://doi.org/10.1177/0963721417746793
Maldonado, M., Dunbar, E., Chemla, E.: Mouse tracking as a window into decision making. Behav. Res. Methods 51(3), 1085–1101 (2019). https://doi.org/10.3758/s13428-018-01194-x
Disposition to trust inventory [McKnight, D.H., Choudhury, V., Kacmar, C.: Developing and validating trust measures for e-commerce: an integrative typology. Inf. Syst. Res. 13(3), 334–359 (2002).] modified to cater to trust in AI instead of trust in people
Sutherland, S.C., Harteveld, C., Young, M.E.: the role of environmental predictability and costs in relying on automation. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (CHI’ 2015). Association for Computing Machinery, New York (2015). https://doi.org/10.1145/2702123.2702609
Madsen, M., Gregor, S.D.: Measuring Human-Computer Trust (2000)
Peirce, J.W.: PsychoPy--Psychophysics software in Python. J. Neurosci. Methods 162(1–2), 8–13, 15 May 2007. https://doi.org/10.1016/j.jneumeth.2006.11.017. Epub 2007 Jan 23. PMID: 17254636; PMCID: PMC2018741
Ghose, U., Arvind, A.S.: Titoghose/Pytrack. GitHub. https://github.com/titoghose/PyTrack. Accessed 15 Mar 2023
Wook Chae, S., Chang Lee, K.: Exploring the effect of the human brand on consumers’ decision quality in online shopping. Online Inf. Rev. 37(1), 83–100 (2013). https://doi.org/10.1108/14684521311311649
Jenkins, Q., Jiang, X.: Measuring trust and application of eye tracking in human robotic interaction. In: Industrial Engineering Research Conference (2010)
Lu, Y.: Detecting and overcoming trust miscalibration in real time using an eye-tracking based technique (dissertation) (2020)
Lu, Y., Sarter, N.: Eye tracking: a process-oriented method for inferring trust in automation as a function of priming and system reliability. IEEE Trans. Hum.-Mach. Syst. 49(6), 560–568 (2019). https://doi.org/10.1109/thms.2019.2930980
Lu, Y., Sarter, N.: Modeling and inferring human trust in automation based on real- time eye tracking data. In: Proceedings of the Human Factors and Ergonomics Society Annual Meeting, vol. 64(1), pp. 344–348 (2020). https://doi.org/10.1177/1071181320641078
Salvucci, D.D., Goldberg, J.H.: Identifying fixations and saccades in eye-tracking protocols. In: Proceedings of the Symposium on Eye Tracking Research & Applications - ETRA 2000 (2000). https://doi.org/10.1145/355017.355028
Steil, J., Huang, M.X., Bulling, A.: Fixation detection for head-mounted eye tracking based on visual similarity of gaze targets. In: Proceedings of the 2018 ACM Symposium on Eye Tracking Research & Applications (2018). https://doi.org/10.1145/3204493.3204538
Stuart, S., et al.: Eye-tracker algorithms to detect saccades during static and dynamic tasks: a structured review. Physiol. Meas. 40(2) (2019). https://doi.org/10.1088/1361-6579/ab02ab
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Bandara, D., Sau, S. (2023). Are Scrutiny and Mistrust Related? An Eye-Tracking Study. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 Posters. HCII 2023. Communications in Computer and Information Science, vol 1832. Springer, Cham. https://doi.org/10.1007/978-3-031-35989-7_68
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