Toward Human-Centered AI Management: Methodological Challenges and Future Directions

36 Pages Posted: 10 May 2023

See all articles by Mengchen Dong

Mengchen Dong

Center for Humans and Machines, Max Planck Institute for Human Development

Jean-Francois Bonnefon

University of Toulouse 1 - Toulouse School of Economics Institute for Advanced Studies/Harvard Law School LWP

Iyad Rahwan

Max Planck Society for the Advancement of the Sciences - Center for Humans and Machines

Date Written: May 10, 2023

Abstract

As algorithms powered by Artificial Intelligence (AI) are increasingly involved in the management of organizations, it becomes imperative to understand people’s feelings and behaviors when machines gain power over humans. There are two mainstream methods for doing so, vignette studies and case studies. Both can reveal important insights into human-centered AI management, but they also yield inconsistent findings, for example on the attitude people have toward AI management. We discuss how the respective limitations of the two methods may be the drivers of these inconsistent findings, and emphasize the advantages of a third method for mitigating these limitations: field experiments on crowdsourced marketplaces. Such field experiments go beyond using crowdsourced marketplaces as human research subject pools, and use them instead as models of workplaces where workers can experience actual AI management under different configurations. Through a proof-of-concept study on Amazon Mechanical Turk (Mturk; as a world-leading crowdsourcing platform), we showed unique human reactions to AI management, which were not perfectly aligned with those in vignettes or case studies. We suggest that field experiments on crowdsourced marketplaces can provide participants with the actual experience of AI management, facilitating robust predictions and allowing for timely behavioral research on AI-powered workflows and organizations.

Keywords: Artificial Intelligence, algorithmic management, algorithm aversion, algorithm appreciation, future of work, work design, crowdsourcing

Suggested Citation

Dong, Mengchen and Bonnefon, Jean-Francois and Rahwan, Iyad, Toward Human-Centered AI Management: Methodological Challenges and Future Directions (May 10, 2023). Available at SSRN: https://ssrn.com/abstract=4444322 or http://dx.doi.org/10.2139/ssrn.4444322

Mengchen Dong (Contact Author)

Center for Humans and Machines, Max Planck Institute for Human Development ( email )

Berlin
Germany
14195 (Fax)

Jean-Francois Bonnefon

University of Toulouse 1 - Toulouse School of Economics Institute for Advanced Studies/Harvard Law School LWP ( email )

21 allée de Brienne
31015 Toulouse cedex 6 France
Toulouse, 31015
France

Iyad Rahwan

Max Planck Society for the Advancement of the Sciences - Center for Humans and Machines ( email )

Berlin
Germany

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