ABSTRACT
Trust is a significant factor in user adoption of new systems. However, although trust is a dynamic attitude of the user towards the system and changes over time, trust in intelligent systems is typically captured as a single quantitative measure at the conclusion of a task. This paper challenges this approach. We report a case study that employed a combination of repeated quantitative and qualitative measures to examine how trust in an intelligent system evolved over time and whether this varied depending on whether the system offered explanations. We discovered different patterns in participants' trust journeys. When provided with explanations, participants' trust levels initially increased, before returning to their original level. Without explanations, participants' trust reduced over time. The qualitative data showed that perceived system ability was more important in determining trust amongst with-explanation participants and perceived transparency was a greater influence on the trust of participants who did not receive explanations. The findings provide a deeper understanding of the development of user trust in intelligent systems and indicate the value of the approach adopted.
- Bunt, A., Lount, M., and Lauzon, C. Are explanations always important?: A study of deployed, low-cost intelligent interactive systems. In Proc. IUI 2012, ACM Press (2012), 169--178. Google ScholarDigital Library
- Cramer, H., Evers, V., Ramlal, S., van Someron, M., Rutledge, L, Stash, N., Aroyo, L., and Wielinga, B. The effects of transparency on trust in and acceptance of a content-based art recommender. User Modeling and User-Adapted Interaction 18, 5 (2008), 455--496. Google ScholarDigital Library
- Gambetta, D. Can We Trust? In D. Gambetta (Ed.) Trust: Making and Breaking Cooperative Relations, Blackwell (1988), 213--237.Google Scholar
- Glass, A., McGuinness, D.L., and Wolverton, M. Toward establishing user trust in adaptive agents. In Proc. IUI'08, ACM Press (2008), 227--236. Google ScholarDigital Library
- Hoffmann, A., Söllner, M., Hoffmann, H., and Leimeister, J.M. Towards Trust-Based Software Requirement Patterns. In Requirements Patterns (RePa), 2012 IEEE Second International Workshop on, IEEE (2012), 7--11.Google ScholarCross Ref
- Höök, K. Steps to take before intelligent systems become real. Interacting with Computers 12, 4 (2000), 409--426.Google ScholarCross Ref
- Kramer, R.M. Trust and distrust in organizations: Emerging perspectives, enduring questions. Annual Review of Psychology, 50, 1 (1999), 569--598.Google ScholarCross Ref
- Kulesza, T., Stumpf, S., Burnett, M., Wong, W., Riche, Y., Moore, T., Oberst, I., Shinsel, A., and McIntosh, K. Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs. In Proc. VL/HCC 2010, IEEE (2010), 41--48. Google ScholarDigital Library
- Lewis, J.D., and Weigert, A.J. Trust as a social reality. Social Forces, 63, 4 (1985), 967--985.Google ScholarCross Ref
- McKnight, D.H., and Chervany, N.L. Trust and distrust definitions: One bite at a time. In Proc. Workshop on Deception, Fraud, and Trust in Agent Societies (2001), 27--54. Google ScholarDigital Library
- Muir, B.M. Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics, 37, 11 (1994), 1905--1922.Google ScholarCross Ref
- Pu, P. and Chen, L. Trust building with explanation interfaces. In Proc. IUI'06, ACM Press (2006), 93--100. Google ScholarDigital Library
- Rempel, R.E., Holmes, J.G., and Zanna, M.P. Trust in close relationships. Journal of Personality and Social Psychology, 49, 1 (1985), 95--112.Google ScholarCross Ref
- Söllner, M., Hoffmann, A., Hoffmann, H., and Leimeister, J.H. How to Use Behavioral Research Insights on Trust for HCI System Design. Ext. Abstracts CHI 2012, ACM Press (2012), 1703--1708. Google ScholarDigital Library
- Tang, J., Gao, H., Liu, H., and Das Sarma, A. eTrust: Understanding trust evolution in an online world. In Proc. SIGKDD 2012, ACM (2012), 253--261. Google ScholarDigital Library
- Tintarev, N., and Masthoff, J. Evaluating the effectiveness of explanations for recommender systems. User Modeling and User-Adapted Interaction, 22, 4-5 (2012), 399--439. Google ScholarDigital Library
Index Terms
- User Trust in Intelligent Systems: A Journey Over Time
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