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

32 Pages Posted: 2 Apr 2020

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

With the increasing availability of data, the adoption of algorithms has become almost a necessity for businesses. Since algorithms often require human involvement, understanding how humans perceive algorithms is instrumental to the success of algorithm design in operations. In particular, the growing concern that algorithms may reproduce or even magnify inequality historically exhibited by humans calls for research about how people perceive the fairness of algorithmic decisions relative to alternative decision-making methods. We study how an algorithmic (vs. human-based) task assignment process changes task recipients' fairness perceptions and, subsequently, work productivity. We conducted a 15-day randomized field experiment with Alibaba Group in a warehouse where workers pick products based on orders known as "pickbills". Half of the workers were randomly assigned to receive their pickbills from a machine that ostensibly relied on an algorithm to distribute pickbills. The other half received pickbills from a human distributor. Despite using the same underlying rule to assign pickbills to workers in both groups, workers perceived the algorithmic assignment process as more fair than the human-based assignment process, causing a difference in perceived fairness by 0.94-1.02 standard deviations. This resulted in further productivity benefits: receiving tasks from an algorithm (relative to a human) significantly increased workers' picking efficiency by 17.3%-19.2%. The productivity gain from the algorithmic assignment was larger for more educated workers and workers who cared more about the difficulty of their pickbills, groups for which perceived fairness has a stronger effect on productivity. We replicated the main results in a second experiment.

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 )

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
58
Abstract Views
213
rank
393,767
PlumX Metrics