New Research Finds Racial Bias in Rideshare Platforms: Minority Riders are Twice as Likely to Have Rides Canceled than Caucasians

INFORMS Journal Management Science New Study Key Takeaways:

• Under-represented minorities are more than twice as likely to have a ride canceled compared to Caucasians.
• The racial bias is lessened during peak demand times.
• Rides are more likely to be canceled for people who show support for the LGBT community with no changes during peak demand times.

CATONSVILLE, MD, May 6, 2020 – New research to be published in the INFORMS journal Management Science has found popular rideshare platforms exhibit racial and other biases that penalize under-represented minorities and others seeking to use their services.

The study, “When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms,” was conducted by Jorge Mejia of Indiana University and Chris Parker of American University. In addition to finding racial biases persist, similar phenomena were also documented against people who show support for the LGBT community. 

Data was analyzed from a major rideshare platform in Washington, D.C., between early October to mid-November 2018. The experiment manipulated rider names and profile pictures to observe drivers’ behavior patterns in accepting and canceling rides. To illustrate support for LGBT rights a rainbow profile picture filter was used. In addition, times of ride requests varied to determine how peak and non-peak price periods impact bias.

“We found under-represented minorities are more than twice as likely to have a ride canceled than Caucasians, that’s about 3% versus 8%,” said Mejia, an assistant professor in the Kelley School of Business at Indiana. “Along with racial bias, LGBT biases are persistent, while there is no evidence of gender bias.” 

Peak timing was found to have a moderating effect, with lower cancelation rates for minority riders, but the timing doesn’t appear to change the bias for riders that signal support for the LGBT community.

“Data-driven solutions may exist wherein rider characteristics are captured when a driver cancels, and the platform penalizes the driver for the biased behavior. One possible way to punish drivers is to move them down the priority list when they exhibit biased cancelation behavior, so they have fewer ride requests. Alternatively, less-punitive measures may provide ‘badges’ for drivers that exhibit especially low cancelation rates for minority riders,” concluded Mejia.

Link to full study.

About INFORMS and Management Science

Management Science is a premier peer-reviewed scholarly journal focused on research using quantitative approaches to study all aspects of management in companies and organizations. It is published by INFORMS, the leading international association for operations research and analytics professionals. More information is available at www.informs.org or @informs.

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