The Trust Recovery Journey. The Effect of the Timing of Errors on the Willingness to Follow AI Advice.
Authors
Complementing human decision-making with AI advice offers substantial advantages. However, humans do not always trust AI advice appropriately and are overly sensitive to incidental AI errors, even in cases with overall good performance. Today's research still needs to uncover the underlying aspects of trust decline and recovery over time in repeated human-AI interactions. Our work investigates the consequences of incidental AI error on (self-reported) trust and participants' reliance on AI advice. Results from our experiment, where 208 participants evaluated 14 legal cases before and after receiving algorithmic advice, showed that trust significantly decreased after early and late errors but was rapidly restored in both scenarios. Reliance significantly dropped only for early errors but not for late errors. In both scenarios, reliance was able to be restored. Results suggest that late (compared to early) errors are less drastic in trust loss and allow quicker recovery. These findings align with an interpretation in which humans can build up trust over time if a system is performing well, making them more tolerant of incidental AI errors.
Research Questions / Practical Problems
Question signals indexed for this paper.
Research Questions
3- How does users' trust in AI dynamically change when AI recommendations contain early versus late errors?Category: Trust Dynamics, Error Timing, and Long-Term RelianceSimilar questionsarrow_forward
- How does error timing affect users' reliance behavior on AI recommendations?Category: Trust Dynamics, Error Timing, and Long-Term RelianceSimilar questionsarrow_forward
- How do historical trust and interaction experiences shape future human-AI interaction behavior?Category: Trust Dynamics, Error Timing, and Long-Term RelianceSimilar questionsarrow_forward
Practical Problems
1- Users easily lose trust due to errors during long-term AI use, affecting subsequent reliance.Category: Trust Dynamics, Error Timing, and Long-Term RelianceSimilar questionsarrow_forward
- 80%
Farsight: Fostering Responsible AI Awareness During AI Application Prototyping
CHI '24· Explainable AI (XAI) +2
- 80%
Trust in AI-assisted Decision Making: Perspectives from Those Behind the System and Those for Whom the Decision is Made
CHI '24· Explainable AI (XAI) +2
- 80%
"AI enhances our performance, I have no doubt this one will do the same": The Placebo effect is robust to negative descriptions of AI
CHI '24· Explainable AI (XAI) +2
- 80%
Trusting Autonomous Teammates in Human-AI Teams - A Literature Review
CHI '25· Explainable AI (XAI) +2
- 75%
For What It's Worth: Humans Overwrite Their Economic Self-Interest to Avoid Bargaining With AI Systems
CHI '22· AI-Assisted Decision-Making & Automation +1
- 67%
Knowing About Knowing: An Illusion of Human Competence Can Hinder Appropriate Reliance on AI Systems
CHI '23· Explainable AI (XAI) +2
- 67%
"When Two Wrongs Don't Make a Right" - Examining Confirmation Bias and the Role of Time Pressure During Human-AI Collaboration in Computational Pathology
CHI '25· Explainable AI (XAI) +2
- 67%
RiskRAG: A Data-Driven Solution for Improved AI Model Risk Reporting
CHI '25· Explainable AI (XAI) +2
- 67%
“I Don’t Think RAI Applies to My Model” – Engaging Non-champions with Sticky Stories for Responsible AI Work
CHI '26· AI Ethics, Fairness & Accountability +2
- 67%
Do People Appropriately Rely on AI-Advice? An Analytical Review of HCI Research on Human-AI Decision-Making
CHI '26· AI-Assisted Decision-Making & Automation +2
Based on Jaccard similarity of research subtopics & professions (≥60%)