Can an AI algorithm mitigate racial economic inequality? - Zhang, Shunyuan,Mehta, Nitin,Singh, Param Vir,Srinivasan, Kannan ([University of Toronto - Rotman School of Management], 2021)
We study the effect of Airbnb’s smart-pricing algorithm on the racial disparity in the daily revenue earned by Airbnb hosts. Our empirical strategy exploits Airbnb’s introduction of the algorithm and its voluntary adoption by hosts as a quasi-natural experiment. Among those who adopted the algorithm, the average nightly rate decreased by 5.7%, but average daily revenue increased by 8.6%. Before Airbnb introduced the algorithm, white hosts earned 12.16 more in daily revenue than Black hosts, controlling for observed characteristics of the hosts, properties, and locations. Conditional on its adoption, the revenue gap between white and Black hosts decreased by 71.3%. However, Black hosts were significantly less likely than white hosts to adopt the algorithm, so at the population level, the revenue gap increased after the introduction of the algorithm. We show that the algorithm’s price recommendations are not affected by the host’s race—but we argue that the algorithm’s race-blindness may lead to pricing that is sub- optimal, and more so for Black hosts than for white hosts. We also show that the algorithm’s effectiveness at mitigating the Airbnb revenue gap is limited by the low rate of algorithm adoption among Black hosts. We offer recommendations with which policy makers and Airbnb may advance smart-pricing algorithms in mitigating racial economic disparities
10.2139/ssrn.3770371
Keywords: Graue Literatur; Pricing Algorithm; Artificial Intelligence; Mitigating Racial Disparity; EconomicGap; Sharing Economy; Airbnb; Algorithm Adoption