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TRUVACE RECORD VERSION
record: TRV-2026-2518
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-11-12T07:46:29.276228Z
status: published
lens: g_space
sector: science
headline: When artificial intelligence apologizes: Dynamic trust updating and the role of personality.
dek: Artificial intelligence (AI) systems are susceptible to error, and many are designed to apologize.Yet, it remains unclear which apologies restore user trust, a question requiring repeated interactions with an AI agent and models capturing how trust evolves across encounters.Drawing on models of apology from human-human interaction research, we examined whether apology components that repair interpersonal trust function similarly in AI.Participants (N = 245) solved five business problems with a chatbot preprogram…
gain_title: In a randomized experiment with 245 participants, a chatbot expressing regret after an error was the only one of six apology types that softened the immediate drop in trust compared with no apology.
problem_title: (none)
trace_subject: (none)
gain_reading: In a randomized experiment with 245 participants, a chatbot expressing regret after an error was the only one of six apology types that softened the immediate drop in trust compared with no apology.
gain_evidence: only Expression of Regret buffered the typical immediate decline in trust
problem_reading: (none)
problem_evidence: (none)
quick_read: Researchers randomly assigned 245 participants to work through five business problems with a chatbot that was preprogrammed to make mistakes on two tasks. After the errors, participants saw no apology or one of six apology strategies, and the team tracked how trust changed after each task.

Trust trajectories differed significantly by apology condition. Compared with no apology, only Expression of Regret buffered the usual immediate fall in trust after an error. The other five strategies did not do so, and none of the apologies boosted trust further once the chatbot went back to answering correctly.

The effects varied by person. Apologies reduced trust loss more among participants whose post-interaction evaluations were more relational than functional. Personality also mattered: Agreeableness and Openness were linked to baseline trust, and Openness was also tied to how trust shifted over time.
limitation: None of the apology conditions improved trust recovery once the chatbot resumed correct performance, and the effects were selective and context-dependent, depending on participants' evaluation profiles and personality.
tag: Evidence-backed gain
key_points: The experiment randomized 245 participants into seven conditions: error with no apology, or error followed by one of six apology strategies. | The six strategies were Expression of Regret, Explanation, Acknowledgment of Responsibility, Declaration of Repentance, Offer of Repair and Request for Forgiveness. | Only Expression of Regret cushioned the immediate trust decline following the error-apology-correction sequence relative to no apology. | No apology type further improved trust recovery once the chatbot resumed correct performance. | Participants with more relational than functional evaluation profiles showed larger apology-related reductions in trust loss; Openness was linked to dynamic trust updating.
rundown: Participants solved five business problems with a chatbot that erred in two tasks at fixed intervals. A dynamic trust-updating model estimated how trust shifted after each trial, separating errors from correct performance across repeated encounters.

Results showed that apology effects were selective. Expression of Regret alone softened the immediate loss of trust, while explanation, taking responsibility, repentance, an offer of repair and a request for forgiveness did not outperform silence on that measure.

In separate analyses, all five Big Five traits were associated with baseline trust. When the traits were considered together, only Agreeableness and Openness stayed associated with baseline trust, and Openness was also linked to dynamic trust updating.
sources:
- peer_reviewed | Technology, Mind, and Behavior | https://doi.org/10.1037/tmb0000230 | 2026-10-05
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