AI apology: a critical review of apology in AI systems.
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| Title: | AI apology: a critical review of apology in AI systems. |
|---|---|
| Authors: | Harland, Hadassah1,2 (AUTHOR) h.harland@research.deakin.edu.au, Dazeley, Richard1 (AUTHOR) richard.dazeley@deakin.edu.au, Senaratne, Hashini2 (AUTHOR) hashini.senaratne@data61.csiro.au, Vamplew, Peter3 (AUTHOR) p.vamplew@federation.edu.au, Cruz, Francisco4,5 (AUTHOR) f.cruz@unsw.edu.au, Nakisa, Bahareh1 (AUTHOR) bahar.nakisa@deakin.edu.au |
| Source: | Artificial Intelligence Review. Dec2025, Vol. 58 Issue 12, p1-78. 78p. |
| Subjects: | Apologizing, Artificial intelligence, Applied sciences, Critical analysis, Human-machine systems, Interdisciplinary research |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 188478430 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: AI apology: a critical review of apology in AI systems. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Harland%2C+Hadassah%22">Harland, Hadassah</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> h.harland@research.deakin.edu.au</i><br /><searchLink fieldCode="AR" term="%22Dazeley%2C+Richard%22">Dazeley, Richard</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> richard.dazeley@deakin.edu.au</i><br /><searchLink fieldCode="AR" term="%22Senaratne%2C+Hashini%22">Senaratne, Hashini</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> hashini.senaratne@data61.csiro.au</i><br /><searchLink fieldCode="AR" term="%22Vamplew%2C+Peter%22">Vamplew, Peter</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> p.vamplew@federation.edu.au</i><br /><searchLink fieldCode="AR" term="%22Cruz%2C+Francisco%22">Cruz, Francisco</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> f.cruz@unsw.edu.au</i><br /><searchLink fieldCode="AR" term="%22Nakisa%2C+Bahareh%22">Nakisa, Bahareh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> bahar.nakisa@deakin.edu.au</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Artificial+Intelligence+Review%22">Artificial Intelligence Review</searchLink>. Dec2025, Vol. 58 Issue 12, p1-78. 78p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Apologizing%22">Apologizing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Applied+sciences%22">Applied sciences</searchLink><br /><searchLink fieldCode="DE" term="%22Critical+analysis%22">Critical analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Human-machine+systems%22">Human-machine systems</searchLink><br /><searchLink fieldCode="DE" term="%22Interdisciplinary+research%22">Interdisciplinary research</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Artificial Intelligence Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=188478430 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10462-025-11305-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 78 StartPage: 1 Subjects: – SubjectFull: Apologizing Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Applied sciences Type: general – SubjectFull: Critical analysis Type: general – SubjectFull: Human-machine systems Type: general – SubjectFull: Interdisciplinary research Type: general Titles: – TitleFull: AI apology: a critical review of apology in AI systems. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Harland, Hadassah – PersonEntity: Name: NameFull: Dazeley, Richard – PersonEntity: Name: NameFull: Senaratne, Hashini – PersonEntity: Name: NameFull: Vamplew, Peter – PersonEntity: Name: NameFull: Cruz, Francisco – PersonEntity: Name: NameFull: Nakisa, Bahareh IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 12 Text: Dec2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 02692821 Numbering: – Type: volume Value: 58 – Type: issue Value: 12 Titles: – TitleFull: Artificial Intelligence Review Type: main |
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