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

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.