Is It Fair to be Accurate? Moral-Emotional Responses to Organizations’ AI Orientation Choices

Flore Vancompernolle Vromman, Corentin Hericher, Corentin Vande Kerckhove and Nicolas Raineri ()
Additional contact information
Flore Vancompernolle Vromman: UCLouvain - Université Catholique de Louvain = Catholic University of Louvain
Corentin Hericher: UCLouvain - Université Catholique de Louvain = Catholic University of Louvain
Corentin Vande Kerckhove: UCLouvain - Université Catholique de Louvain = Catholic University of Louvain
Nicolas Raineri: ICN Business School, CEREFIGE - Centre Européen de Recherche en Economie Financière et Gestion des Entreprises - UL - Université de Lorraine

Post-Print from HAL

Abstract: While research on algorithmic decision-making has grown substantially, little is known about people's moral reactions to organizations' artificial intelligence (AI) orientation choices. Drawing on deonance theory, we hypothesize that an organization's choice between an algorithm maximizing accuracy at the expense of fairness and one prioritizing fairness over accuracy triggers distinct moral-emotional responses among third-party observers. We conducted three vignette-based experiments comparing accuracy- and fairness-oriented algorithms in hiring (Studies 1 and 3) and dismissal (Study 2), with different degrees of accuracy loss (Study 3). Results indicate that moral emotions (i.e., other-condemning and other-praising) mediate the effects of this choice on observers' behavioral responses (i.e., negative and positive word-of-mouth) toward the organization. By highlighting how accuracy–fairness trade-offs shape observers' moral appraisals of organizations, this article advances management research on algorithmic decision-making and extends deonance theory to algorithmic human resource management, establishing AI orientation choices as a moral context informing observers' approval or disapproval of organizations.

Keywords: moral emotions; ethical AI; deonance theory; accuracy-fairness trade-off; algorithmic fairness (search for similar items in EconPapers)
Date: 2026-05-24
Note: View the original document on HAL open archive server: https://hal.science/hal-05631981v1
References: Add references at CitEc
Citations:

Published in Business and Society, 2026, ⟨10.1177/00076503261448488⟩

Downloads: (external link)
https://hal.science/hal-05631981v1/document (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-05631981

DOI: 10.1177/00076503261448488

Access Statistics for this paper

More papers in Post-Print from HAL
Bibliographic data for series maintained by CCSD ().