Racial bias present in ride share pricing algorithms: study

Authors Aylin Caliskank Akshat Pandey

June 14, 2020

Article by Shannon Mallard of The GW Hatchet

[GW] School of Engineering and Applied Science researchers found evidence of racial bias in the algorithms ride share companies use to price fares, according to a study published last week.
 
The study shows that factors like race, ethnicity, age, housing prices and education level can influence the algorithms that determine fares for services like Uber and Lyft. The authors, assistant professor of computer science Aylin Caliskan and graduate teaching assistant Akshat Pandey, said in an interview with the technology publication Venture Beat that the data used to develop “dynamic pricing” algorithms – which adjust rates based on variables like location and route length – could result in discriminatory pricing practices.
 
“When machine learning is applied to social data, the algorithms learn the statistical regularities of the historical injustices and social biases embedded in these data sets,” Caliskan and Pandey said in the interview.
 
The authors analyzed a ride share data sample from Chicago, choosing the city because local officials recently adopted a law requiring ride-hailing apps to disclose fare prices, according to the study. The city’s ride hailing data also includes pick-up and drop-off locations that the researchers correlated with data from the U.S. Census Bureau’s American Community Survey, the study states.
 
The study shows that fares were higher for drop-offs in neighborhoods with large non-White populations. The authors found an increase in ride-share prices when riders received a high school education or less, lived in homes priced under the Chicago median or were picked up or dropped off in neighborhoods with a low percentage of residents over 40, according to the study.
 
“Our findings imply that using dynamic pricing can lead to biases based on the demographics of neighborhoods where ride-hailing is most popular,” Caliskan and Pandey said. “If neighborhoods with more young people use ride-hailing applications more, getting picked up or dropped off in those neighborhoods will cost more, as in our findings for the city of Chicago.”