Built a Tool to Audit Shipt
Once the most valuable US semiconductor company, Intel has missed most of the AI wave that has propelled the share prices of its competitors.
In early 2020, gig workers for the app-based delivery company Shipt noticed something strange about their paychecks. The company, which had been acquired by Target in 2017 for US $550 million, offered same-day delivery from local stores. Those deliveries were made by Shipt workers, who shopped for the items and drove them to customers’ doorsteps. Business was booming at the start of the pandemic, as the COVID-19 lockdowns kept people in their homes, and yet workers found that their paychecks had become…unpredictable. They were doing the same work they’d always done, yet their paychecks were often less than they expected. And they didn’t know why.
On Facebook and Reddit, workers compared notes. Previously, they’d known what to expect from their pay because Shipt had a formula: It gave workers a base pay of $5 per delivery plus 7.5 percent of the total amount of the customer’s order through the app. That formula allowed workers to look at order amounts and choose jobs that were worth their time. But Shipt had changed the payment rules without alerting workers. When the company finally issued a press release about the change, it revealed only that the new pay algorithm paid workers based on “effort,” which included factors like the order amount, the estimated amount of time required for shopping, and the mileage driven.
The company claimed this new approach was fairer to workers and that it better matched the pay to the labor required for an order. Many workers, however, just saw their paychecks dwindling. And since Shipt didn’t release detailed information about the algorithm, it was essentially a black box that the workers couldn’t see inside.
The workers could have quietly accepted their fate, or sought employment elsewhere. Instead, they banded together, gathering data and forming partnerships with researchers and organizations to help them make sense of their pay data. I’m a data scientist; I was drawn into the campaign in the summer of 2020, and I proceeded to build an SMS-based tool—the Shopper Transparency Calculator—to collect and analyze the data. With the help of that tool, the organized workers and their supporters essentially audited the algorithm and found that it had given 40 percent of workers substantial pay cuts. The workers showed that it’s possible to fight back against the opaque authority of algorithms, creating transparency despite a corporation’s wishes.