Photo Credit: Photo by Thought Catalog on Unsplash
Study Says Uber, Lyft Charged Riders More For Trips To Non-White Neighborhoods
A recent study revealed that Uber and Lyft algorithms may be discriminating against riders in predominantly non-white neighborhoods.
Researchers Aylin Caliskan and Akshat Pandey at the George Washington University in Washington, D.C. analyzed transportation and census data in Chicago that assessed possible racial disparities in how much passengers paid for a ride based on location.
The two analyzed more than 100 million trips between November 2018 and December 2019. More than half of the trips analyzed in their data set were made by individual riders.
They found that the algorithm used by the ride-hailing companies Lyft and Uber charged higher prices per mile for a trip if the destination or pick-up location has a higher percentage of non-white residents, low-income residents, or highly educated residents, as reported in Salon.
“While demand and speed have the highest correlation with ride-hailing fares, analysis shows that users of ride-hailing applications in the city of Chicago may be experiencing social bias with regard to fare prices when they are picked up or dropped off in neighborhoods with a low percentage of individuals over 40 or a low percentage of individuals with a high school diploma or less,” the two concluded.
This isn’t the first time that Lyft or Uber has faced criticism with their algorithms.
A different study commissioned in 2016 found a pattern of racial and gender discrimination among drivers for Uber, Lyft, and Flywheel. Male customers with perceived African American names were more than twice as likely to have drivers cancel their rides compared to white passengers.
Women with African-American sounding names experienced similar results, according to the study.
A representative from Lyft told Salon in a statement, “This analysis is deeply flawed. The researcher acknowledges that the study was not based on actual demographic data of rideshare users. In fact, the study makes clear that speed and demand have the highest correlation with algorithmically generated fares and that individual demographic data is neither available to rideshare companies nor used in the algorithms that determine pricing. There are many factors that go into dynamic pricing — race is not one of them. We appreciate the researchers’ attempt to study unintentional bias, but this study misses the mark.”