Jennifer: He says fraud management in places like this has changed quite a bit since he started this job. For example, they used to set up filters based on some simple questions.
David Cost: Was it a customer that we had ever seen before, right? We had certain flags that we would look for and somebody would manually go look at those orders and kind of have to just make a guess on whether they thought that order was good or not. We ship it out and if it turns out it’s not good, then we, the retailer, you know, take that loss. So it’s as if somebody stole those goods. It’s the same thing as shoplifting. You know, and it can, it can have a real impact on your business and you want to be careful. If you’re too cautious, you turn good business away. I mean, there’s nothing worse than taking a customer who’s got a good valid form of payment and telling them, nope. Sorry. Doesn’t look quite right, so we’re not gonna ship it.
Jennifer: One place Rainbow came up against this… was in doing business with buyers in other countries.
David Cost: So they’re placing the order from one of the islands in the Caribbean. So their billing address is a Caribbean address, they’re shipping the goods mainly to a Florida or a Texas re-shipper’s address where a bunch of orders are combined to go to the island, right? And then they’re taken into those islands. These are good customers but for traditional fraud, to be able to see that foreign credit card, foreign address, but a US delivery address would raise all kinds of alarm bells. So the sophistication of the AI now to be able to help us accurately figure out what’s a good order and what’s a bad order, lets us raise our level of customer service… So that we’re taking care of customers at the same time, minimizing the potential loss to the business, with those orders that we would fulfill and ship and then not get paid for.
Jennifer: Rainbow is using a tool from fraud detection company, Signifyd… and he says these days they turn down less than 1% of their orders.
David Cost: In earlier times when we had to do this all manually… they would go to Google Maps and look up the address to see if the address looked legitimate. You know, now with the sophistication of the tech to know who you are, where else have we seen you order, digital IDs on your device, distance between your billing and shipping address. So many factors. So again, even five or six years ago, that process was manual and it could take a day or two, you know, now they’re being made in fractions of a second.
Will Douglas Heaven: I mean, this happened to me yesterday, actually, you know, I got a text message. I’d taken my car to the garage and, you know, put a larger than usual sum on it.
Jennifer: That’s Will Douglas Heaven, Tech Review’s senior A-I editor.
The Download: metaverse fashion, and looser covid rules in China
Fashion creator Jenni Svoboda is designing a beanie with a melted cupcake top, sprinkles, and doughnuts for ears. But this outlandish accessory isn’t destined for the physical world—Svoboda is designing for the metaverse. She’s working in a burgeoning, if bizarre, new niche: fashion stylists who create or curate outfits for people in virtual spaces.
Metaverse stylists are increasingly sought-after as frequent users seek help dressing their avatars—often in experimental, wildly creative looks that defy personal expectations, societal standards, and sometimes even physics.
Stylists like Svoboda are among those shaping the metaverse fashion industry, which is already generating hundreds of millions of dollars. But while, to the casual observer, it can seem outlandish and even obscene to spend so much money on virtual clothes, there are deeper, more personal, reasons why people are hiring professionals to curate their virtual outfits. Read the full story.
Making sense of the changes to China’s zero-covid policy
On December 1, 2019, the first known covid-19 patient started showing symptoms in Wuhan. Three years later, China is the last country in the world holding on to strict pandemic control restrictions. However, after days of intense protests that shocked the world, it looks as if things could finally change.
Beijing has just announced wide-ranging relaxations of its zero covid policy, including allowing people to quarantine at home instead of in special facilities for the first time.
Uber’s facial recognition is locking Indian drivers out of their accounts
Uber checks that a driver’s face matches what the company has on file through a program called “Real-Time ID Check.” It was rolled out in the US in 2016, in India in 2017, and then in other markets. “This prevents fraud and protects drivers’ accounts from being compromised. It also protects riders by building another layer of accountability into the app to ensure the right person is behind the wheel,” Joe Sullivan, Uber’s chief security officer, said in a statement in 2017.
But the company’s driver verification procedures are far from seamless. Adnan Taqi, an Uber driver in Mumbai, ran into trouble with it when the app prompted him to take a selfie around dusk. He was locked out for 48 hours, a big dent in his work schedule—he says he drives 18 hours straight, sometimes as much as 24 hours, to be able to make a living. Days later, he took a selfie that locked him out of his account again, this time for a whole week. That time, Taqi suspects, it came down to hair: “I hadn’t shaved for a few days and my hair had also grown out a bit,” he says.
More than a dozen drivers interviewed for this story detailed instances of having to find better lighting to avoid being locked out of their Uber accounts. “Whenever Uber asks for a selfie in the evenings or at night, I’ve had to pull over and go under a streetlight to click a clear picture—otherwise there are chances of getting rejected,” said Santosh Kumar, an Uber driver from Hyderabad.
Others have struggled with scratches on their cameras and low-budget smartphones. The problem isn’t unique to Uber. Drivers with Ola, which is backed by SoftBank, face similar issues.
Some of these struggles can be explained by natural limitations in face recognition technology. The software starts by converting your face into a set of points, explains Jernej Kavka, an independent technology consultant with access to Microsoft’s Face API, which is what Uber uses to power Real-Time ID Check.
“With excessive facial hair, the points change and it may not recognize where the chin is,” Kavka says. The same thing happens when there is low lighting or the phone’s camera doesn’t have a good contrast. “This makes it difficult for the computer to detect edges,” he explains.
But the software may be especially brittle in India. In December 2021, tech policy researchers Smriti Parsheera (a fellow with the CyberBRICS project) and Gaurav Jain (an economist with the International Finance Corporation) posted a preprint paper that audited four commercial facial processing tools—Amazon’s Rekognition, Microsoft Azure’s Face, Face++, and FaceX—for their performance on Indian faces. When the software was applied to a database of 32,184 election candidates, Microsoft’s Face failed to even detect the presence of a face in more than 1,000 images, throwing an error rate of more than 3%—the worst among the four.
It could be that the Uber app is failing drivers because its software was not trained on a diverse range of Indian faces, Parsheera says. But she says there may be other issues at play as well. “There could be a number of other contributing factors like lighting, angle, effects of aging, etc.,” she explained in writing. “But the lack of transparency surrounding the use of such systems makes it hard to provide a more concrete explanation.”
The Download: Uber’s flawed facial recognition, and police drones
One evening in February last year, a 23-year-old Uber driver named Niradi Srikanth was getting ready to start another shift, ferrying passengers around the south Indian city of Hyderabad. He pointed the phone at his face to take a selfie to verify his identity. The process usually worked seamlessly. But this time he was unable to log in.
Srikanth suspected it was because he had recently shaved his head. After further attempts to log in were rejected, Uber informed him that his account had been blocked. He is not alone. In a survey conducted by MIT Technology Review of 150 Uber drivers in the country, almost half had been either temporarily or permanently locked out of their accounts because of problems with their selfie.
Hundreds of thousands of India’s gig economy workers are at the mercy of facial recognition technology, with few legal, policy or regulatory protections. For workers like Srikanth, getting blocked from or kicked off a platform can have devastating consequences. Read the full story.
I met a police drone in VR—and hated it
Police departments across the world are embracing drones, deploying them for everything from surveillance and intelligence gathering to even chasing criminals. Yet none of them seem to be trying to find out how encounters with drones leave people feeling—or whether the technology will help or hinder policing work.
A team from University College London and the London School of Economics is filling in the gaps, studying how people react when meeting police drones in virtual reality, and whether they come away feeling more or less trusting of the police.
MIT Technology Review’s Melissa Heikkilä came away from her encounter with a VR police drone feeling unnerved. If others feel the same way, the big question is whether these drones are effective tools for policing in the first place. Read the full story.
Melissa’s story is from The Algorithm, her weekly newsletter covering AI and its effects on society. Sign up to receive it in your inbox every Monday.