Archives 30 June 2021

When U.S. law enforcement officials need to cast a wide net for information, they’re increasingly turning to the vast digital ponds of personal data created by Big Tech companies via the devices and online services that have hooked billions of people around the world.

Data compiled by four of the biggest tech companies shows that law enforcement requests for user information — phone calls, emails, texts, photos, shopping histories, driving routes and more — have more than tripled in the U.S. since 2015. Police are also increasingly savvy about covering their tracks so as not to alert suspects of their interest.

That’s the backdrop for recent revelations that the Trump-era U.S. Justice Department sought data from Apple, Microsoft and Google about members of Congress, their aides and news reporters in leak investigations — then pursued court orders that blocked those companies from informing their targets.

In just the first half of 2020 — the most recent data available — Apple, Google, Facebook and Microsoft together fielded more than 112,000 data requests from local, state and federal officials. The companies agreed to hand over some data in 85% of those cases. Facebook, including its Instagram service, accounted for the largest number of disclosures.

Consider Newport, a coastal city of 24,000 residents that attracts a flood of summer tourists. Fewer than 100 officers patrol the city — but they make multiple requests a week for online data from tech companies.

That’s because most crimes — from larceny and financial scams to a recent fatal house party stabbing at a vacation rental booked online — can be at least partly traced on the internet. Tech providers, especially social media platforms, offer a “treasure trove of information” that can help solve them, said Lt. Robert Salter, a supervising police detective in Newport.

Contract drivers say algorithms terminate them by email—even when they have done nothing wrong.

Stephen Normandin spent almost four years racing around Phoenix delivering packages as a contract driver for Amazon.com Inc. Then one day, he received an automated email. The algorithms tracking him had decided he wasn’t doing his job properly.

The 63-year-old Army veteran was stunned. He’d been fired by a machine.

Normandin says Amazon punished him for things beyond his control that prevented him from completing his deliveries, such as locked apartment complexes. Amazon assigned him some pre-dawn deliveries at apartment complexes when their gates were still locked, a common complaint among Flex drivers. The algorithm instructs drivers in such instances to deliver packages to the main office, but that wasn’t open either. Normandin called the customer as instructed—a long shot because most people don’t answer calls from unfamiliar numbers, especially early morning. He called driver support, which couldn’t get through to the customer either. Meanwhile, the clock was ticking, and the algorithm was taking note.

When Ryan Cope was deactivated in 2019, he didn’t bother arguing or consider paying for arbitration. By then, Cope had already decided there was no way he could meet the algorithms’ demands. Driving miles along winding dirt roads outside Denver in the snow, he often shook his head in disbelief that Amazon expected the customer to get the package within two hours.

When drivers do challenge poor ratings, they can’t tell if they’re communicating with real people. Responses often include just a first name or no name at all, and the replies typically apply to a variety of situations rather than a specific problem. Even if a name is attached, a machine most likely generated the first few email responses, according to people familiar with the matter.

When human managers get involved, they typically conduct a hasty review—if they do one at all—because they must meet their own performance standards. A former employee at a driver support call center said dozens of part-time seasonal workers with little training were assigned to oversee issues for millions of drivers.