Academic Journal
TAMING ARTIFICIAL INTELLIGENCE: A THEORY OF CONTROL-ACCOUNTABILITY ALIGNMENT AMONG AI DEVELOPERS AND USERS.
| Τίτλος: | TAMING ARTIFICIAL INTELLIGENCE: A THEORY OF CONTROL-ACCOUNTABILITY ALIGNMENT AMONG AI DEVELOPERS AND USERS. |
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| Συγγραφείς: | GROTE, GUDELA, K. PARKER, SHARON, CROWSTON, KEVIN |
| Πηγή: | Academy of Management Review; Apr2026, Vol. 51 Issue 2, p278-299, 22p, 1 Color Photograph, 2 Charts |
| Θεματικοί όροι: | Artificial intelligence, Risk management in business, Corporate governance, Computer software developers, Responsibility, Control theory (Engineering) |
| Περίληψη: | The growing agency of artificial intelligence (AI) systems, more specifically systems based on machine learning, has raised concerns about the security, safety, and ethical risks of AI use. We argue that core to mitigating AI risks is proper alignment of control and accountability for the stakeholders involved in AI development and use. Control enables, and accountability motivates, stakeholders to achieve desired and avoid undesired outcomes using AI. However, AI systems’ capabilities for autonomous adaptivity reduce control even for the experts who create them. Moreover, increasing interdependencies between AI development and use render it difficult to unambiguously locate control and accountability. In this paper, we address these challenges for mitigating AI risks by postulating decentralized forms of stakeholder governance and integrative negotiations among stakeholders during the AI life cycle as conducive to aligning control and accountability for AI development and use. Further, we specify that extensive information sharing aided by perspective taking and a shared norm of accountability facilitate integrative negotiation strategies. We conclude by discussing the implications of our theory for management scholarship on the impact of AI, and identify promising avenues for future research at micro, meso, and macro levels of analysis. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: TAMING ARTIFICIAL INTELLIGENCE: A THEORY OF CONTROL-ACCOUNTABILITY ALIGNMENT AMONG AI DEVELOPERS AND USERS. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22GROTE%2C+GUDELA%22">GROTE, GUDELA</searchLink><br /><searchLink fieldCode="AR" term="%22K%2E+PARKER%2C+SHARON%22">K. PARKER, SHARON</searchLink><br /><searchLink fieldCode="AR" term="%22CROWSTON%2C+KEVIN%22">CROWSTON, KEVIN</searchLink> – Name: TitleSource Label: Source Group: Src Data: Academy of Management Review; Apr2026, Vol. 51 Issue 2, p278-299, 22p, 1 Color Photograph, 2 Charts – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+management+in+business%22">Risk management in business</searchLink><br /><searchLink fieldCode="DE" term="%22Corporate+governance%22">Corporate governance</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+developers%22">Computer software developers</searchLink><br /><searchLink fieldCode="DE" term="%22Responsibility%22">Responsibility</searchLink><br /><searchLink fieldCode="DE" term="%22Control+theory+%28Engineering%29%22">Control theory (Engineering)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: The growing agency of artificial intelligence (AI) systems, more specifically systems based on machine learning, has raised concerns about the security, safety, and ethical risks of AI use. We argue that core to mitigating AI risks is proper alignment of control and accountability for the stakeholders involved in AI development and use. Control enables, and accountability motivates, stakeholders to achieve desired and avoid undesired outcomes using AI. However, AI systems’ capabilities for autonomous adaptivity reduce control even for the experts who create them. Moreover, increasing interdependencies between AI development and use render it difficult to unambiguously locate control and accountability. In this paper, we address these challenges for mitigating AI risks by postulating decentralized forms of stakeholder governance and integrative negotiations among stakeholders during the AI life cycle as conducive to aligning control and accountability for AI development and use. Further, we specify that extensive information sharing aided by perspective taking and a shared norm of accountability facilitate integrative negotiation strategies. We conclude by discussing the implications of our theory for management scholarship on the impact of AI, and identify promising avenues for future research at micro, meso, and macro levels of analysis. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Academy of Management Review is the property of Academy of Management 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.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 278 Subjects: – SubjectFull: Artificial intelligence Type: general – SubjectFull: Risk management in business Type: general – SubjectFull: Corporate governance Type: general – SubjectFull: Computer software developers Type: general – SubjectFull: Responsibility Type: general – SubjectFull: Control theory (Engineering) Type: general Titles: – TitleFull: TAMING ARTIFICIAL INTELLIGENCE: A THEORY OF CONTROL-ACCOUNTABILITY ALIGNMENT AMONG AI DEVELOPERS AND USERS. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: GROTE, GUDELA – PersonEntity: Name: NameFull: K. PARKER, SHARON – PersonEntity: Name: NameFull: CROWSTON, KEVIN IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 03637425 Numbering: – Type: volume Value: 51 – Type: issue Value: 2 Titles: – TitleFull: Academy of Management Review Type: main |
| ResultId | 1 |