Study Discovers Racial Discrimination By Ride Hailing App Drivers
AUDIE CORNISH, HOST:
Right hailing apps like Uber and Lyft broke out a few years ago as alternatives to taxis. The promise they made to city residents - they would have a car to pick you up no matter who you are, and that appealed to African-Americans in particular. They for years faced discrimination by cab drivers.
But a new study suggests the apps struggle with the very same issue of racial profiling as taxis. NPR's tech blogger Alina Selyukh is here to tell us about the research. Welcome back to the studio.
ALINA SELYUKH, BYLINE: Hello.
CORNISH: So first just tell us how this study worked.
SELYUKH: Right. So this research was done in two cities - Boston and Seattle. And academics there tried to record almost 1,500 ride requests to see if race played a difference in how long people had to wait for a ride. In Seattle, they had eight people four black, four white and split by gender. And in Boston, they had people who could sort of pass up for either race but then used separate accounts with a white-sounding name and then a, quote, "distinctly black" name.
CORNISH: So what did the study find?
SELYUKH: Overall they did find some differences in how passengers were treated. In Seattle, African-American riders had to wait a bit longer to get a ride. In Boston, people with the black-sounding names were more likely to have drivers cancel a ride after accepting it. That was especially the case for black men. And it's important to note, though, that Lyft did a fair bit better. Its drivers did not appear to cancel on riders in a way that factored in race.
CORNISH: All right, so there's a difference between Uber and Lyft, but what's going on there? What is the difference?
SELYUKH: And that's actually a point that researchers make in their study. There's a difference when Uber and Lyft drivers find out what your name is and what you look like. On Uber, they learn that after accepting your request, meaning they have to cancel it if they want to pass you up. On Lyft, they see your name and face before they even accept the request, meaning if they do decide to ignore you, that kind of data would not be recorded in this research.
Of course Lyft does not like this interpretation, and they say that its drivers have a financial incentive. They get bonuses if they do accept as many requests as possible.
CORNISH: So how have the companies actually responded to this study?
SELYUKH: They both really highlight the fact that they have made a positive impact on people of color. They're giving people a new way to get around without having to hail a cab, which is a historically discriminatory practice. And both companies do have non-discrimination policies.
But here's the tricky part to me with these companies. While the driver's behavior reflects broadly on them, the choices that go into that behavior are really personal. It is one individual making a quick decision about what kind of passenger they want in their own car.
CORNISH: These companies aren't really the first to face this problem, right? I mean Airbnb is trying to weed out discrimination on its platform. So is this something that the gig economy needs to deal with more broadly?
SELYUKH: Let's be clear. This is not just a problem for the gig economy. But here, it is a special kind of challenge for Uber and Lyft. The drivers are not employees. They're independent participants on the app just like the riders. It's very similar on Airbnb. So these companies - they can crack down on particular users, say, who are overtly racist. Typically that would be prompted by another user reporting them, and they could be banned from an app, for instance.
But it's much harder to weed out subtle discrimination that doesn't have a record or piece of data attached to them. The researchers on the study suggest that the companies, for instance, could replace names and attendees with unique numbers to see that levels the playing field. It's unclear whether the apps will attempt that, but I guess we'll see.
CORNISH: That's NPR's tech blogger Alina Selyukh. Thanks so much.
SELYUKH: Thank you.
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