Discrimination based on driver bias remains a major issue for Uber, Lyft and other ridesharing services

FOR IMMEDIATE RELEASE

Newswise — BLOOMINGTON, Ind. -- New research from the Indiana University Kelley School of Business suggests that discrimination based on driver bias remains a major issue for Uber, Lyft and other ridesharing services.

Companies have sought to address the problem by removing information about a rider’s gender and race from ride requests, hoping to eliminate bias when a ride is requested. However, bias is a factor when drivers cancel after a request is accepted.

The study, presented in the London School of Economics Business Review, by Jorge Mejia, an assistant professor of operations and decision sciences at IU’s Kelley School of Business, and Chris Parker, an assistant professor of supply chain and information systems at Penn State University's Smeal College of Business, found that underrepresented minorities are nearly twice as likely to have a ride cancelled by drivers.

Individuals expressing support for gay, lesbian, bisexual and transgendered groups also experienced also significantly higher cancellation rates. Their results are based on 1,600 ride requests on a major ridesharing platform in a North American city. Biases related to gender appear to have been eliminated.

Their research had four main takeaways:

• Timing of communicating information to service providers must be carefully considered. While the aim is to minimize and eliminate bias, if it does exist, it may be better if the bias occurs earlier in the process (at the ride request stage) rather than later (post-acceptance). Cancelled riders also incur costs related to waiting before the cancellation occurs and the inconvenience of re-requesting a ride. Rider characteristics either need to be fully hidden until the last possible moment for rider and driver to be safely connected, or fully visible from the ride request stage.
• It is possible that a data-driven solution exists wherein rider characteristics are captured when a driver cancels and is penalized by the platform for biased behavior. One possible way to punish drivers is to move them down the priority list when they exhibit biased cancellation behavior so they have fewer ride requests.
• Despite public opinion polls indicating that support for the LGBT community is strong, there are still negative associations that can impact supporters. Since ridesharing platforms usually are connected rider’s social media accounts, such as Facebook, their support for a social cause – sometimes seen when a person places a rainbow filter on their profile picture – may affect the quality of the service they receive.
• They looked at dynamic pricing and if the incentives offered by higher prices can reduce the extent of such bias. Their findings suggest that may be necessary for ridesharing platforms to adopt a dynamic fee structure that incentivizes drivers to accept rides from social groups or in specific areas of cities that are traditionally underserved.

Mejia and Parker believe ridesharing platforms are responsible for addressing driver’s biased behaviors. The algorithms they use to connect riders and drivers should enable them to do so. They conclude: “If a ridesharing company has drivers who consistently favor a certain demographic, to what extent is the platform complicit and potentially legally responsible for the biased behavior … It may be necessary to increase firms’ cost of discrimination. One would expect that, like the drivers with whom they contract, firms would respond to increased costs with better policies and monitoring of biased behavior.”

Professor Mejia is available for interviews and can be reached at (812) 855-5286 or [email protected].