Workshop on Trust and Reliance in AI-Human Teams (TRAIT)
Open accessAbstract
As humans increasingly interact (and even collaborate) with AI systems during decision-making, creative exercises, and other tasks, appropriate trust and reliance are necessary to ensure proper usage and adoption of these systems. Specifically, people should understand when to trust or rely on an algorithm’s outputs and when to override them. Significant research focus has aimed to define and measure trust in human-AI interaction, and design and implement interactions that promote and calibrate trust. However, conceptualizing trust and reliance, and identifying the best ways to measure these constructs and effectively shape them in human-AI interactions remains a challenge, especially across contexts and domains.
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Research is missing theoretical and empirical evidence on how explanations influence AoR.
“However, research is missing theoretical and empirical evidence on how explanations influence AoR [5].”
From Appropriate Reliance on AI Advice: Conceptualization and the Effect of Explanations (2023) · cites this paper as [5]
Cites
Human trust in artificial intelligence: Review of empirical research
Glikson, E., & Woolley, A. W. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0057 · Cited by 2,921 · see citing works
Zhang, Y., Liao, Q. V., & Bellamy, R. K. E. (2020) · Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency · doi:10.1145/3351095.3372852 · Cited by 895 · see citing works
Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
Bansal, G., Wu, T., Zhou, J., Fok, R., Nushi, B., Kamar, E., Ribeiro, M. T., & Weld, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445717 · Cited by 757 · cited 1× · see citing works
“Consequently, interest in human-AI interaction and collaboration is ex-ploding both within and beyond the HCI community [1, 9, 22, 24].”
Self-citationManipulating and Measuring Model Interpretability
Poursabzi-Sangdeh, F., Goldstein, D. G., Hofman, J. M., Wortman Vaughan, J. W., & Wallach, H. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445315 · Cited by 728 · see citing works
How Much Information?: Effects of Transparency on Trust in an Algorithmic Interface
Kizilcec, R. F. (2016) · Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems · doi:10.1145/2858036.2858402 · Cited by 615 · see citing works
Expanding Explainability: Towards Social Transparency in AI systems
Ehsan, U., Liao, Q. V., Muller, M., Riedl, M. O., & Weisz, J. D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445188 · Cited by 538 · cited 1× · see citing works
“…as independent of its developers, in real-world organizational settings, system trust may in fact depend on the user’s trust in those developers [6], and humans may choose to rely on an AI system for reasons unrelated to their trust in those systems (e.g., organizational pressures or…”
Understanding the Effect of Accuracy on Trust in Machine Learning Models
Yin, M., Wortman Vaughan, J., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300509 · Cited by 527 · cited 1× · see citing works
“For example, many studies have started to investigate the impact that system transparency and accuracy have on trust [4, 7, 11, 25]; the role of trust in the user experience of complex algorithmic systems, such as the impact on satisfaction, adoption, and reliance [5, 15]; and the potentially…”
The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
Bussone, A., Stumpf, S., & O'Sullivan, D. (2015) · 2015 International Conference on Healthcare Informatics · doi:10.1109/ichi.2015.26 · Cited by 441 · see citing works
Self-citationYang, Q., Steinfeld, A., & Zimmerman, J. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300468 · Cited by 247 · see citing works
A Case for Humans-in-the-Loop: Decisions in the Presence of Erroneous Algorithmic Scores
De-Arteaga, M., Fogliato, R., & Chouldechova, A. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376638 · Cited by 221 · see citing works
Levy, A., Agrawal, M., Satyanarayan, A., & Sontag, D. (2021) · Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3411764.3445522 · Cited by 73 · see citing works
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- Authors
- Bansal, G., Buçinca, Z., Holstein, K., Hullman, J., Smith-Renner, A. M., Stumpf, S., & Wu, S.
- Affiliations
- Microsoft (United States) (US)
- Harvard University (US)
- Carnegie Mellon University (US)
- Northwestern University (US)
- AI Organization, Dataminr, United States
- University of Glasgow (GB)
- Human-Computer Interaction Institute, Carnegie Mellon University, United States
- ORCID
- Venue
- Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2023-04-19
- Pages
- 1-6
- OpenAlex
- W4366527500
- Cited by
- 8
- References
- 11
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 3
- FWCI
- 4.00
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) (publishedVersion)
- Topics
- Social Sciences › Psychology › Social Psychology › Human-Automation Interaction and Safety
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- trust in AI, AI reliance, human-AI teaming, human-AI interaction, trust measurement, trust calibration, algorithmic decision-making, human-AI collaboration, artificial intelligence adoption
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