Study reveals racial, LGBT bias persists in ridesharing drivers despite mitigation efforts

Despite efforts by ridesharing companies to eliminate or reduce discrimination, research from the Indiana University Kelley School of Business finds that racial and LGBT bias persists among drivers.

Despite efforts by ridesharing compes to eliminate or reduce discrimination, research from the Indiana University Kelley School of Business finds that racial and LGBT bias persists among drivers.

Platforms such as Uber, Lyft and Via responded to drivers' biased behaviour by removing information that could indicate a rider's gender and race from initial ride requests. However, researchers still found that biases against underrepresented groups and those who indicate support for the LGBT community continued to exist after drivers accepted a ride request -- when the rider's picture would then be displayed.

In other words, their efforts shifted some of the biased behaviour until after the ride was confirmed, resulting in higher cancellation rates. Understanding whether bias has been removed also is important for ridesharing compes as they not only compete against each other but also with traditional transportation options.

"Our results confirm that bias at the ride request stage has been removed. However, after ride acceptance, racial and LGBT biases are persistent, while we found no evidence of gender biases," said Jorge Mejia, assistant professor of operations and decision technologies. "We show that signalling support for a social cause -- in our case, the lesbian, gay, bisexual and transgender community -- can also impact service provision. Riders who show support for the LGBT community, regardless of race or gender, also experience significantly higher cancellation rates."

Mejia and co-author Chris Parker, assistant professor in the information technology and analytics department at American University in Washington, believe they are the first to use support for social causes as a bias-enabling characteristic. Their article, "When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms," is published in Management Science.

They performed a field experiment on a ridesharing platform in fall 2018 in Washington, D.C. They randomly mpulated rider names, using those traditionally perceived to be white or Black, as well as profile pictures to observe drivers' behaviour patterns in accepting and cancelling rides. To illustrate support for LGBT rights, the authors overlaid a rainbow filter on the rider's picture profile.

"We found that underrepresented minorities are more than twice as likely to have a ride cancelled than Caucasians; that's about 3 per cent versus 8 per cent," Mejia said. "There was no evidence of gender bias." Mejia and Parker also varied times of ride requests to study whether peak price periods affected bias. They found that higher prices associated with peak times alleviated some of the bias against riders from the underrepresented group, but not against those who signal support for the LGBT community.

They believe that ridesharing compes should use other data-driven solutions to take note of rider characteristics when a driver cancels and penalize the driver for biased behaviour. One possible way to punish drivers is to move them down the priority list when they exhibit biased cancellation behaviour, so they have fewer ride requests. Alternatively, less-punitive measures may provide "badges" for drivers who exhibit especially low cancellation rates for minority riders.

But, ultimately, policymakers may need to intervene, Mejia said.

"Investments in reducing bias may not occur orgcally, as ridesharing platforms are trying to maximize the number of participants in the platform -- they want to attract both riders and drivers," he said. "As a result, it may be necessary for policymakers to mandate what information can be provided to a driver to ensure an unbiased experience, while maintaining the safety of everyone involved, or to create policies that require ridesharing platforms to monitor and remove drivers based on biased behaviour.

"Careful attention should be paid to these policies both before and after implementation, as unintended consequences are almost sure to follow any simple fix."

( With inputs from ANI )