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Algorithms, leadership, and morality: why a mere human effect drives the preference for human over algorithmic leadership

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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.

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Notes

  1. We rely on the leadership literature to conceptualize legitimacy, fairness, and trustworthiness perceptions as a function of leadership style and behavior [5, 24, 112]. This literature focuses on the subjective perceptions of employees regarding whether their leader makes decisions in a way that is legitimate, fair, and trustworthy. These perceptions are important because they have far-reaching consequences for an organization’s reputation and whether people accept decisions made by their leader. For example, if we consider fairness perceptions of an employee inside an organization, whether this employee perceives that he/she is treated fairly by their leader can predict the likelihood that he/she will quit their job [31], engage in unethical behavior [114], and even become a whistleblower and speak out against an organization’s malpractices [98]. Therefore, these perceptions are a useful lens from which to understand leader support/acceptance and organizational reputation.

  2. An a priori power analysis suggests that approximately 176 total observations are required to achieve 90% power at an α of .05 (Cohen’s f = 0.3; [21, 22]. However, due to our instrumental attention check exclusion criteria, we narrowly missed our sample size objective. We used more stringent criteria for both type I and II errors, relative to conventional parameters (e.g., 80% power), as using Prolific to recruit participants allows us to obtain a larger sample size.

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Authors and Affiliations

  1. Department of Management and Organisation, NUS Business School, National University of Singapore, 15 Kent Ridge Drive, Singapore, 119245, Singapore

    Jack McGuire & David De Cremer

  2. Department of Management and Organisation, NUS Business School, Cambridge University, 15 Kent Ridge Drive, Singapore, 119245, Singapore

    David De Cremer

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Appendices

Appendix A

figure a

The image above illustrates the hierarchical representation of the simulated organization that participants were recruited to work for. All participants were allocated the position of “Employee 2” on what appeared to be a random basis.

Appendix B

figure b

The image above illustrates the series of loading bars that appeared on the screen for participants. The loading bar was a gif-image where the dark blue square looped from left to right. After several seconds, the subsequent screen appeared so that participants saw that the counter (e.g., 1/2) went up. This animated progress bar was used to make participants believe that the system is busy connecting them, and that they had to wait until the system was ready.

Appendix C

figure c

This is an illustration that was provided to participants of the algorithmic code that resulted in the decision to choose Bauer Industries as a sponsor in the business moral dilemma. The purpose of the illustration was to bolster the realism of our manipulation and give participants the impression they were interacting with an algorithmic authority.

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McGuire, J., De Cremer, D. Algorithms, leadership, and morality: why a mere human effect drives the preference for human over algorithmic leadership. AI Ethics 3, 601–618 (2023). https://doi.org/10.1007/s43681-022-00192-2

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

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