Toward human-centered AI management: Methodological challenges and future directions

As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream meth...

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Bibliographic Details
Published in:Technovation Vol. 131; p. 102953
Main Authors: Dong, Mengchen, Bonnefon, Jean-François, Rahwan, Iyad
Format: Journal Article
Language:English
Published: Elsevier Ltd 01.03.2024
Elsevier
Subjects:
Algorithm appreciation
Algorithm aversion
Algorithmic management
Artificial intelligence
Cognitive science
Crowdsourcing
Future of work
Work design
Crowdsourcing
Algorithm aversion
Work design
Algorithm appreciation
Artificial intelligence
Algorithmic management
Future of work
ISSN:0166-4972, 1879-2383
Online Access:Get full text
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Summary:As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to conduct human-centered AI management research and understand people's feelings and behaviors when machines gain power over humans. The two mainstream methods – vignette studies and case studies – reveal important but inconsistent insights. Here we discuss the respective limitations of vignette studies (affective forecasting errors, biased media coverage, and question substitution) and case studies (social desirability biases and lack of random assignment and control conditions), which may lead them to overrate negative and positive reactions to AI management, respectively. We further discuss the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. A proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform) showed unique human reactions to AI management, which were not perfectly aligned with those in vignette or case studies. Participants (N = 504) did not differ significantly under AI versus human management, in terms of performance, intrinsic motivation, fairness perception, and commitment. We suggest that crowdsourced marketplaces can go beyond human research subject pools and become models of AI-managed workplaces, facilitating timely behavioral research and robust predictions on human-centered work designs and organizations. •Human reactions to AI management can be biased in vignette and case studies.•Vignette studies are biased by forecasting error, media coverage, and question substitution.•Case studies are biased by social desirability and lack of randomization and control.•Field experiments on crowdsourced marketplaces can mitigate these limitations.•One field experiment on Mturk shows similar human reactions to AI and human managers.
ISSN:0166-4972
1879-2383
DOI:10.1016/j.technovation.2024.102953