Cited by 19Social Robot Interaction and HRIAI in Service InteractionsSpeech and dialogue systems

The DOI matches its Crossref record; 222 of the 222 references it cites were found in a registry or an index.

AI apology: a critical review of apology in AI systems

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Abstract

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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Crossref · 10.1007/s10462-025-11305-8

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Research works printed but not linked: 25

  1. Almuttalibi N (2016) The forms and functions of apologies with indicative references references to the letters of Keats and Byron. Int J Eng Lit 6(February):11–22
  2. Amodei D, Olah C, Steinhardt J, et al. (2016) Concrete problems in AI safety. ArXiv
  3. Aydin M (2013) Cross cultural pragmatics: a study of apology speech acts by Turkish speakers, American English speakers and advance nonnative speakers of English in Turkey. PhD thesis, Minnesota State University - Mankato
  4. Benner D, Elshan E, Schöbel S, et al. (2021) What do you mean? A review on recovery strategies to overcome conversational breakdowns of conversational agents. In: Int Conf on Inf Syst (ICIS), pp 1–17
  5. Eslami-Rasekh A, Mardani M (2010) Investigating the effects of teaching apology speech act, with a focus on intensifying strategies, on pragmatic development of EFL learners: The Iranian context. Int J Lang Soc Cult 30(1):96–103
  6. Gert B, Gert J (2020) The definition of morality. In: Zalta EN (ed) Stanford Encycl of Philos, fall, 2020th edn. Stanford University, Metaphysics Research Lab
  7. Hamacher A (2015) Believing in BERT: making good on bad robot behavior. PhD thesis, University College London
  8. Harris AM, DeChurch L (2023) “Alexa, how can I trust you again?” Trust repair in human-AI teams. PhD thesis, United States– Illinois
  9. Jelínek M, Fischer K (2023) Trust regulation in social robotics: from violation to repair. [ArXiv]
  10. Joyce R (1999) Apologizing. Public Aff Q 13(2):159–173
  11. Khavas ZR (2021) A review on trust in human-robot interaction. [ArXiv]
  12. Kreiter R (2023) Can we forgive a robotic teammate? The role of trust and trust repair strategies in human-agent teams. PhD thesis, University of Twente
  13. Lazare A (2004) On apology. Oxford University Press
  14. Lee Y, Bae JE, Kwak SS, et al. (2011) The effect of politeness strategy on human - robot collaborative interaction on malfunction of robot vacuum cleaner. In RSS workshop on HRI
  15. Li M, Lee JD (2023) Measure and manage trust in human-AI conversations. PhD thesis, University of Winconsin, Madison
  16. Lu PPY, Konishi M, Sano S, et al. (2022) What makes an apology more effective? Exploring anthropomorphism, individual differences, and emotion in human-automation trust repair. [ArXiv]
  17. Na G, Choi J, Kang H (2023) It’s Not my fault, but I’m to blame: the effect of a home robot’s attribution and approach movement on trust and emotion of users. Int J Hum-Comput Interact 10:2209977
  18. Perkins R, Khavas ZR, Robinette P (2021) Trust calibration and trust respect: a method for building team cohesion in human robot teams. [ArXiv]
  19. Rzepka C, Berger B (2018) User interaction with AI-enabled systems: a systematic review of IS research. ICIS 2018 proc
  20. Soares N, Fallenstein B (2014) Aligning superintelligence with human interests: a technical research agenda. Tech. rep, Machine Intelligence Research Institute (MIRI) technical report
  21. Stowers K (2017) The role of accounts and apologies in mitigating blame toward human and machine agents. PhD thesis, University of Central Florida
  22. Wilson L, Mechenbier MX, Melonçon L (2020) Affective investment. Acad Labor: Res Artist 4(6) · DOI
  23. Wright B, Collins E, Cameron D (2022) When robots interact with groups, where does the trust reside? [ArXiv]
  24. Yu D, Li L, Su H, et al. (2023) Assessing the potential of AI-assisted pragmatic annotation: the case of apologies. [ArXiv]
  25. Zhang X (2023) “Sorry, it was my fault”: repairing trust in human-robot interactions. Int J Hum-Comput Stud 175(103031):79

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Metadata

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
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
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
  • Social Sciences › Business, Management and Accounting › Marketing › 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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