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Erschienen in: Social Justice Research 1/2022

01.12.2021

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

verfasst von: David De Cremer, Jack McGuire

Erschienen in: Social Justice Research | Ausgabe 1/2022

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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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Fußnoten
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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Metadaten
Titel
Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)
verfasst von
David De Cremer
Jack McGuire
Publikationsdatum
01.12.2021
Verlag
Springer US
Erschienen in
Social Justice Research / Ausgabe 1/2022
Print ISSN: 0885-7466
Elektronische ISSN: 1573-6725
DOI
https://doi.org/10.1007/s11211-021-00382-z

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