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Ridehailing applications that collect mobility data from individuals toinform smart city planning predict each trip's fare pricing with automatedalgorithms that rely on artificial intelligence (AI). This type of AIalgorithm, namely a price discrimination algorithm, is widely used in theindustry's black box systems for dynamic individualized pricing. Lackingtransparency, studying such AI systems for fairness and disparate impact hasnot been possible without access to data used in generating the outcomes ofprice discrimination algorithms. Recently, in an effort to enhance transparencyin city planning, the city of Chicago regulation mandated that transportationproviders publish anonymized data on ridehailing. As a result, we present thefirst large-scale measurement of the disparate impact of price discriminationalgorithms used by ridehailing applications.  The application of random effects models from the meta-analysis literaturecombines the city-level effects of AI bias on fare pricing from census tractattributes, aggregated from the American Community Survey. An analysis of 100million ridehailing samples from the city of Chicago indicates a significantdisparate impact in fare pricing of neighborhoods due to AI bias learned fromridehailing utilization patterns associated with demographic attributes.Neighborhoods with larger non-white populations, higher poverty levels, youngerresidents, and high education levels are significantly associated with higherfare prices, with combined effect sizes, measured in Cohen's d, of -0.32,-0.28, 0.69, and 0.24 for each demographic, respectively. Further, our methodshold promise for identifying and addressing the sources of disparate impact inAI algorithms learning from datasets that contain U.S. geolocations.

Disparate Impact of Artificial Intelligence Bias in Ridehailing Economy's Price Discrimination Algorithms