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Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

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Abstract

Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n = 237; Study 2, n = 684), we show that humans consider the collaboration with autonomous algorithms as unfair when the algorithm leads decision-making and will even incur high financial costs in order to avoid this. Our results also show that humans do not want to exclude algorithms entirely but seem to prefer a 60–40% human–algorithm partnership. These findings contrast the position taken by today’s emerging business model on the issue of automated organizational decision-making. Our findings also provide support for the existence of an implicit theory—held by both present and future employees—that humans should lead and algorithms follow.

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Notes

  1. In order to check if participants read the two plan options correctly, we asked, “Please indicate below which strategic plan enables the middle manager to have a voice in this decision” and provided the two plans as response options. For this question 33 participants incorrectly, selected option 1 and were, therefore, removed from our analyses.

  2. For exploratory reasons, participants were also asked which plan made them feel respected, dignified, and confident, but since our main focus is on understanding cooperation and fairness we only discuss those in the main text.

  3. For exploratory reasons, participants were also asked how much they agreed with, felt satisfied and confident with, and how effective they perceived the proposed partnership, but since our main focus is on understanding cooperation and fairness, we only discuss those in the main text.

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Authors and Affiliations

  1. Department of Management and Organisation, Centre on AI Technology for Humankind (AiTH), NUS Business School, National University of Singapore, 15 Kent Ridge Drive, Singapore, 119245, Singapore

    David De Cremer & Jack McGuire

  2. Cambridge University, Cambridge, UK

    David De Cremer

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  1. David De Cremer
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Correspondence to David De Cremer.

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Appendix

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Fig. 7
figure 7

The image depicts the loading bars that were presented to participants in the experimental paradigm. Each bar between the dotted lines was presented on the screen individually as a gif-image in which the dark blue squares moved from left to right in continuous loops for sporadic intervals of time, upon which the subsequent loading bar would be presented (in order from 1/4 to 4/4). The sequence of this presentation created the impression that the system was in the process of connecting participants with other users

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De Cremer, D., McGuire, J. Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!). Soc Just Res 35, 33–55 (2022). https://doi.org/10.1007/s11211-021-00382-z

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