The Impacts of Algorithmic Work Assignment on Fairness Perceptions and Productivity: Evidence from Field Experiments

48 Pages Posted: 2 Apr 2020 Last revised: 25 Jun 2023

See all articles by Bing Bai

Bing Bai

Washington University in St. Louis - John M. Olin Business School

Hengchen Dai

University of California, Los Angeles (UCLA) - Anderson School of Management

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School

Fuqiang Zhang

Washington University in St. Louis - John M. Olin Business School

Haoyuan Hu

Alibaba Group

Date Written: March 8, 2020

Abstract

Problem Definition: We study how algorithmic (vs. human-based) task assignment processes change task recipients' fairness perceptions and productivity.
Academic/Practical Relevance: Since algorithms are widely adopted by businesses and often require human involvement, understanding how humans perceive algorithms is instrumental to the success of algorithm design in operations. Particularly, the growing concern that algorithms may reproduce inequality historically exhibited by humans calls for research about how people perceive the fairness of algorithmic decision-making relative to traditional human-based decision-making and, consequently, adjust their work behaviors.
Methodology: In a 15-day-long field experiment with Alibaba Group in a warehouse where workers pick products following orders (or “pick lists”), we randomly assigned half of the workers to receive pick lists from a machine that ostensibly relied on an algorithm to distribute pick lists, and the other half to receive pick lists from a human distributor.
Results: Despite that we used the same underlying rule to assign pick lists in both groups, workers perceive the algorithmic (vs. human-based) assignment process as fairer by 0.94-1.02 standard deviations. This yields productivity benefits: receiving tasks from an algorithm (vs. a human) increases workers' picking efficiency by 17.35%-19.39%. These findings persist beyond the first day when workers were involved in the experiment, suggesting that our results are not limited to the initial phrase when workers might find algorithmic assignment novel. We replicate the main results in another field experiment involving a nonoverlapping sample of warehouse workers. We also show via online experiments that people in the U.S. also view algorithmic task assignment as fairer than human-based task assignment.
Managerial Implications: We demonstrate that algorithms can have broader impacts beyond offering greater efficiency and accuracy than humans: introducing algorithmic assignment processes may enhance fairness perceptions and productivity. This insight can be utilized by managers and algorithm designers to better design and implement algorithm-based decision making in operations.

Keywords: Behavioral Operations, Field Experiment, Productivity, Fairness, Artificial Intelligence

JEL Classification: C93, J24, O33

Suggested Citation

Bai, Bing and Dai, Hengchen and Zhang, Dennis and Zhang, Fuqiang and Hu, Haoyuan, The Impacts of Algorithmic Work Assignment on Fairness Perceptions and Productivity: Evidence from Field Experiments (March 8, 2020). Available at SSRN: https://ssrn.com/abstract=3550887 or http://dx.doi.org/10.2139/ssrn.3550887

Bing Bai (Contact Author)

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Hengchen Dai

University of California, Los Angeles (UCLA) - Anderson School of Management ( email )

110 Westwood Plaza
Los Angeles, CA 90095-1481
United States

Dennis Zhang

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

Fuqiang Zhang

Washington University in St. Louis - John M. Olin Business School ( email )

One Brookings Drive
Campus Box 1133
St. Louis, MO 63130-4899
United States

HOME PAGE: http://www.olin.wustl.edu/faculty/zhang/

Haoyuan Hu

Alibaba Group ( email )

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