Management Research Seminar Series, conducted by the Department of Economics and Business, aims to bring accomplished researchers in the Management field to share their current research projects to facilitate an academic discussion, enhance knowledge, and discover potential connections. The series is designed for the academic audience, i.e., the Faculty and MA, PhD students; however, anyone interested in the series is welcome to attend.
On March 29, Dr. Gülen Sarial Abi from Copenhagen Business School will join the Management Research Seminar Series. The event will be on Zoom. For details, please email firstname.lastname@example.org or Yurteri_Sidar@phd.ceu.edu.
Title: When Algorithms Harm: Consumers’ Responses following Algorithmic Bias
Abstract: Algorithms are widely used in marketing contexts including segmentation and targeting, product recommendations, pricing and advertising. Despite the efficiency gained through algorithmic technologies, there is increasing concern that algorithms may be biased, resulting in unfair outcomes for members of some protected classes. We examine how consumers respond to a brand that uses an algorithm following algorithmic bias resulting in algorithmic discrimination toward members of their protected class. Extending developments in the social categorization literature and its role in discrimination, we propose that consumers will respond negatively to a brand when the output of an algorithm used by the brand results in discrimination (vs. preferential treatment) toward members of their protected class. Consumers’ perceptions of the extent to which the algorithm uses social categorization that humans use in their decision-making will mediate the negative responses following bias that results in discrimination (vs. preferential treatment). We further propose that three sources of algorithmic bias (i.e., technical, emergent, and preexisting) will moderate consumers’ responses to the brand using the algorithm following algorithmic discrimination (vs. preferential treatment). The results from one secondary data study and five pre-registered experiments support the proposed theory and hypotheses. The findings generate insights on algorithmic bias and extend the literature on algorithmic marketing and also generate actionable guidelines for managerial practice.