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

TL;DR

This study analyzes the disparate impact of AI-driven fare pricing algorithms in ridehailing services using large-scale data from Chicago, revealing significant biases linked to neighborhood demographics and demonstrating a method for transparency and fairness assessment.

Contribution

First large-scale measurement of AI bias in ridehailing fare algorithms using city-mandated anonymized data and meta-analysis techniques to identify demographic disparities.

Findings

Neighborhoods with higher non-white populations face higher fares

Poverty and younger residents are associated with increased prices

Methods can identify sources of AI bias in geolocation datasets

Abstract

Ridehailing applications that collect mobility data from individuals to inform smart city planning predict each trip's fare pricing with automated algorithms that rely on artificial intelligence (AI). This type of AI algorithm, namely a price discrimination algorithm, is widely used in the industry's black box systems for dynamic individualized pricing. Lacking transparency, studying such AI systems for fairness and disparate impact has not been possible without access to data used in generating the outcomes of price discrimination algorithms. Recently, in an effort to enhance transparency in city planning, the city of Chicago regulation mandated that transportation providers publish anonymized data on ridehailing. As a result, we present the first large-scale measurement of the disparate impact of price discrimination algorithms used by ridehailing applications. The application of random effects models from the meta-analysis literature combines the city-level effects of AI bias on fare pricing from census tract attributes, aggregated from the American Community Survey. An analysis of 100 million ridehailing samples from the city of Chicago indicates a significant disparate impact in fare pricing of neighborhoods due to AI bias learned from ridehailing utilization patterns associated with demographic attributes. Neighborhoods with larger non-white populations, higher poverty levels, younger residents, and high education levels are significantly associated with higher fare 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 methods hold promise for identifying and addressing the sources of disparate impact in AI algorithms learning from datasets that contain U.S. geolocations.