Ride-hail apps like Uber, Via, and Lyft have made transportation more egalitarian in some ways, reducing the chances a taxi will bypass a person of color for the white customer just down the street, or connecting underserved neighborhoods to the surrounding community. But until self-driving vehicles take over, human bias is still a problem.
After studies found that people of color face longer wait times to be matched with a driver—sometimes 35 percent longer than white riders—by 2018, most ride-hail platforms had responded by limiting the information drivers receive about potential riders according to industry watchers. For many of the services on these platforms, drivers can no longer see the name, profile picture, or drop-off location of customers before accepting a ride. The hope was that discrimination would decrease.
But, as a recent report shows, bias will find a way.
The report, “When Transparency Fails: Bias and Financial Incentives in Ridesharing Platforms” by Jorge Mejia of Indiana University and Chris Parker of American University, found that people from underrepresented minorities are more than twice as likely to have a ride canceled during non-peak hours as whites. While it wasn’t as significant a difference, the study found that riders who signal support for the LGBT community are 1.46 times more likely to be canceled on during non-peak hours, and over half as likely during peak hours.
To get at this, authors called around 3,200 rides in Washington, D.C., from a central Metro stop and indicated a major airport as the final destination. After a ride was accepted, the driver was able to see a profile photo and name. The researchers used photos of real people taken from a database of faces used for research and AI training. They controlled for similar levels of perceived “attractiveness” by using two websites that rate a photo subject’s attractiveness—one by using AI; the other, crowdsourcing. They also assigned the customers names that studies show people associate with a particular race: among them, Allison, Greg, Latoya, and Jamal. To indicate LGBT support, they used an optional rainbow filter that is widely used to indicate affinity for LGBT causes.
Mejia and Parker then observed driver behavior once the ride had been accepted and the rider’s photo and information revealed. They waited three minutes to allow the driver to cancel; if the driver seemed to intend to complete the ride, the researchers canceled the ride and the driver received a cancellation fee. While the authors concluded that the ride-hailing platforms’ decision on when to reveal rider information eliminated bias at the ride-request stage, it seems that it merely shifted the timing of when discrimination strikes.
Peak timing, however, did seem to moderate bias, particularly with people of color. “For underrepresented minorities, we see that the pricing mechanism wins,” Parker, an assistant professor of information technology and analytics, told CityLab. “If you put yourself in the shoes of someone making a biased decision, they might say, ‘For $15, I’m not going to take this person, but for $30, I’ll take this person.’ That’s where the financial mechanism of peak times with higher prices could be beneficial.”
But timing didn’t matter for LGBT supporters: Drivers were almost as likely to cancel on them whether during off-peak or peak timing, the study found. In this scenario, Parker said, it’s difficult to understand where the bias lies. “Is it really biased against LGBT people, or is it against people who feel so strongly about some social cause that they’re going to talk your ear off about it?” said Parker. “If I put the symbol for Greenpeace over my photo, would it have the same effect? As a driver, am I worried they’re going to talk my ear off about the environment? I don’t think that’s what this case is but we can’t rule it out as a scientific explanation.”
Parker thinks that the estimate of driver bias he and Mejia found for LGBT supporters actually might be lower than the national average. “D.C. has a relatively large number of LGBT communities and supporters,” he said. “If you were to go somewhere in the country with fewer, I would expect this effect to be even larger. Similar arguments can be made along the racial dimension.”
Of course, ride-hail companies insist that their drivers are not employees: They are independent contractors who use the platform to connect them to riders. So holding drivers accountable for their biases is difficult. “But either way, the platform is the one that holds the PR risk,” said Parker. “In general, the platform doesn’t want drivers that will provide a bad ride experience.”
Parker said that his and Mejia’s interest in the subject was sparked by a personal experience: Mejia, who identifies as Hispanic, started paying attention to driver bias a few years ago, when he noticed that his wife, who is white, generally had a much shorter wait time when trying to book a ride than he did. (This was before ride-hail platforms shifted the timing of information given to drivers.) One purpose of the study is to help platforms reflect on the type and timing of information they give to drivers in order to reduce experiences like Mejia’s. “We hope to start a conversation about the rider-driver relationship and emphasize the important role of platform governance,” the study reads.
But the results present a dilemma: If drivers continue to receive information only after they’ve accepted the ride, driver bias could lead to longer wait times in the long run. If a driver cancels after the initial acceptance, the rider’s wait to find a driver is even longer than if the driver never accepted the ride. So, has the experience for the rider improved?
The study suggests that company-gathered data on wait times and cancellation rates across demographics should be made available in order to effectively address the problem. “Most of the previous studies before the change [to a later customer reveal] looked at matching times and quoted wait times,” said Parker. “They weren’t even measuring cancellation times, so we don’t have a great comparison of before and after. We need to get into these companies and say, ‘Let us analyze your data; let us figure out what happened.’ Without getting data from before the change and after the change, we’ll have a hard time figuring out where we are now.”
Parker suggested that ride-hail companies think about how to make better matches. “One way to do it is to keep track of the drivers and move them down the priority list when they start exhibiting biases in any of the dimensions that we care about,” he said. “Another way to do it that’s not as punitive is to give some kind of a star or badge system that says ‘LGBT Friendly.’ Instead of punishing someone for being bad, reward someone for treating everyone the same, for acting in a way that we consider to be a pro-social way.”
Corporations need to take action in cases of persistent bias—and not just because it will affect their bottom lines, Parker says. “It’s important to try to make transportation easier and fairer.”