AI apology: a critical review of apology in AI systems
Open accessAbstract
Apologies are a powerful tool used in human-human interactions to provide affective support, regulate social processes, and exchange information following a trust violation. The emerging field of AI apology investigates the use of apologies by artificially intelligent systems, with recent research suggesting how this tool may provide similar value in human-machine interactions. Until recently, contributions to this area were sparse, and these works have yet to be synthesised into a cohesive body of knowledge. This article provides the first synthesis and critical analysis of the state of AI apology research, focusing on studies published between 2020 and 2023. We derive a framework of attributes to describe five core elements of apology: outcome, interaction, offence, recipient, and offender. With this framework as the basis for our critique, we show how apologies can be used to recover from misalignment in human-AI interactions, and examine trends and inconsistencies within the field. Among the observations, we outline the importance of curating a human-aligned and cross-disciplinary perspective in this research, with consideration for improved system capabilities and long-term outcomes.
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Users may accept a CA apology at an emotional level while still perceiving the system as unreliable for future interactions, rendering the decision to continue using the system contingent on unresolved uncertainty about its reliability.
“Consequently, users may accept a CA apology at an emotional level while still perceiving the system as unreliable for future interactions, rendering the decision to continue using the system contingent on unresolved uncertainty about its reliability (Gnewuch & Reinkemeier, 2025;Harland et al., 2025;Kim & Song, 2021).”
From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as Harland et al., 2025
Section 5.2 (page unavailable) distinguishes an apology's emotional appeal from the more critical processing involved in trust repair. Section 4.3.1.7 (page unavailable) reports that an initial trust increase can fade when a system fails to improve. These passages support a possible gap between an apology's emotional effect and confidence in future performance, but do not establish that users emotionally accept an apology while remaining uncertain whether to continue using the system. (LLM, conf=0.78)
Experimental — accuracy not yet measuredEmpirical evidence on the relative effectiveness of human-like and system-like trust repair strategies, particularly when implemented by the AI system itself, remains limited.
“Despite the conceptual tension between”
From Explainability in AI: Comparing Human-Like and System-Like Trust Repair Strategies (2026) · cites this paper as Harland et al., 2025
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Apology as interactional governance in human-AI dialogue: Evidence from bidirectional apologising
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Baihaqi, M. Y., Contreras, A. F. G., & Yoshino, K. (2026) · Advanced Robotics · doi:10.1080/01691864.2026.2721029
Repairing trust in AI: Attribution, anthropomorphism, and the illusion of difference
Kim, P. H., & Ferragamo, A. (2026) · Current Opinion in Psychology · doi:10.1016/j.copsyc.2026.102423
Choosing Between AI Responses: How Valence, Arousal, and Dominance Shape User Preference
Паршаков, П. А., & Paklina, S. N. (2026) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2026.2727770
Whether and how to apologize: A comparative study of apologies,between humans and LLM chatbotsPreprint
Chen, Y., Yang, Y., & Chen, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.7467627
Too Human or Not Enough? User Preferences for AI Error Recovery Messages
Satarenko, A. (2026) · Nordic Conference on Human-Computer Interaction · doi:10.1145/3821402.3830119
Investigating the effects of agent-produced paralinguistic respirations associated with apologies
Maraev, V., Lagerstedt, E., Howes, C., & Pélachaud, C. (2026) · Companion Publication of the International Conference on Multimodal Interaction (ICMI Companion) · doi:10.1145/3776591.3838175
- Authors
- Harland, H., Dazeley, R., Senaratne, H., Vamplew, P., Cruz, F., & Nakisa, B.
- Affiliations
- Commonwealth Scientific and Industrial Research Organisation (AU)
- Deakin University (AU)
- Federation University (AU)
- UNSW Sydney (AU)
- Corresponding author
- Harland, Hadassah
- ORCID
- Venue
- Artificial Intelligence Review
- Published
- 2025-10-06
- Volume
- 58
- Issue
- 12
- ISSN
- 0269-2821 (ISSN-L)
- 1573-7462
- OpenAlex
- W4414849137
- Cited by
- 19
- References
- 222
- Citations by year
- 2026: 15 · 2025: 4
- FWCI
- 21.05
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Artificial Intelligence Review (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- Deakin University
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Keywords
- apology, human-AI interaction, breach of trust, crime, emotional support, social regulation
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