Algorithmic Bias in Service

69 Pages Posted: 20 Aug 2020 Last revised: 30 Nov 2021

See all articles by Kalinda Ukanwa

Kalinda Ukanwa

University of Southern California - Marshall School of Business

Roland T. Rust

University of Maryland - Robert H. Smith School of Business

Date Written: November 22, 2021

Abstract

Research shows that algorithms using sociodemographic data (e.g., race, gender, education, etc.) can produce biased outcomes that cause many consumers to be excluded from or endure lower levels of service. Though research suggests that these algorithms are more profitable than unbiased algorithms that do not use sociodemographic data, prior findings do not consider potential social effects of these algorithms on consumer demand. This research investigates the dynamic outcomes of competition between biased and unbiased algorithms in a market where word-of-mouth influences consumer choice behavior. Relative to unbiased algorithms, this research demonstrates that biased algorithms can be more profitable in the short run but less profitable in the long run, due to consumer word-of-mouth. Models and simulations show that word-of-mouth leads marginalized consumers to gravitate towards easier-to-access unbiased algorithmic services. Non-marginalized consumers, on the other hand, learn they have a relatively easier time accessing services anywhere. When sufficient numbers of marginalized and non-marginalized consumers learn from each other via word-of-mouth, long run demand is greater for unbiased algorithmic services. This research demonstrates that firms that use unbiased algorithms and account for social effects (e.g., word-of-mouth) in the algorithm’s design can reduce algorithmic bias while improving both long-term profits and societal well-being.

Keywords: algorithms, algorithmic bias, algorithmic fairness, discrimination, word of mouth, agent-based modeling

Suggested Citation

Ukanwa, Kalinda and Rust, Roland T., Algorithmic Bias in Service (November 22, 2021). USC Marshall School of Business Research Paper, Available at SSRN: https://ssrn.com/abstract=3654943 or http://dx.doi.org/10.2139/ssrn.3654943

Kalinda Ukanwa (Contact Author)

University of Southern California - Marshall School of Business ( email )

701 Exposition Blvd, HOH 431
Los Angeles, CA California 90089-1424
United States

Roland T. Rust

University of Maryland - Robert H. Smith School of Business ( email )

College Park, MD 20742-1815
United States

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