Publication Type

Journal Article

Version

publishedVersion

Publication Date

5-2023

Abstract

Algorithms are increasingly making decisions in organizations that carry moral consequences and such decisions are considered to be ordinarily made by leaders. An important consideration to be made by organizations is therefore whether adopting algorithms in this domain will be accepted by employees and whether this practice will harm their reputation. Considering this emergent phenomenon, we set out to examine employees’ perceptions about (a) algorithmic decision-making systems employed to occupy leadership roles and make moral decisions in organizations, and (b) the reputation of organizations that employ such systems. Furthermore, we examine the extent to which the decision agent needs to be recognized as “merely” a human, or whether more information is needed about the decision agent’s moral values (in this case, whether it is known that the human leader is humble or not) to be preferred over an algorithm. Our results reveal that participants in the algorithmic leader condition—relative to those in the human leader and humble human leader conditions—perceive the decision made to be less fair, trustworthy, and legitimate, and this in turn produces lower acceptance rates of the decision and more negative perceptions of the organization’s reputation. The human leader and humble human leader conditions do not significantly differ across all main and indirect effects. This latter effect strongly suggests that people prefer human (vs. algorithmic) leadership primarily because they are human and not necessarily because they possess certain moral values. Implications for theory, practice, and directions for future research are discussed.

Keywords

Leadership, algorithms, moral decision-making, ethics

Discipline

Artificial Intelligence and Robotics | Leadership Studies | Organizational Behavior and Theory | Theory and Algorithms

Publication

AI and Ethics

Volume

3

First Page

601

Last Page

618

ISSN

2730-5953

Identifier

10.1007/s43681-022-00192-2

Publisher

Springer

Additional URL

https://doi.org/10.1007/s43681-022-00192-2

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