The Big Data capacity that has fueled companies like Amazon and Uber has also transformed what it means to work for a big company.

These firms increasingly are relying on algorithms to assign tasks and evaluate work, but some workers complain that arbitrary employment decisions are likewise made based on these complex formulas.

Amazon is the most recent company to receive attention for its automated supervision of employees.  Company statements and documents, according to a published report, reveal that Amazon fires about 10% of its warehouse workers annually using data collected about their performance.

Amazon’s system is designed to measure automatically the performance of employees, based on metrics such as the number of packages processed in a given period. Workers who perform under the company’s proprietary performance metrics, which include data points such as customer demand and location, may receive an automatic warning notice if they have fallen below the mandated production standard.

If an employee receives two written notices or six notices in a rolling 12-month period, the system generates an automatic termination notice.

Amazon has argued the objectivity of the performance metrics show that its employment system is unbiased. “The criteria for receiving a production related notice is entirely objective. The production system generates all production related warnings and termination notices automatically with no input from supervisors,” Crystal Carey, an attorney for Morgan Lewis says in a legal document defending Amazon’s decision-making process for firing workers.

External critics, however, have expressed concerns about the extent to which Amazon is using machine intelligence to make employment decisions about its workers.

“One of the things that we hear consistently from workers is that they are treated like robots because they’re monitored and supervised by these automated systems,” saysStacy Mitchell, co-director of the Institute for Local Self-Reliance. “They’re monitored and supervised by robots.”

Meanwhile, Uber recently won a victory in federal court against drivers who argued that the ride-sharing company’s level of machine-learning supervision constituted an illegal employment relationship. The drivers contested that classification, insisting they are freelancers and thus not subject to rules regarding employees.

They also complain that Uber’s algorithm exerts too much control over their lives, says Andrew J. Hawkins.

The Uber ruling essentially validates the company’s argument that algorithm-based management is a legal way for a company to provide oversight of workers while still being able to classify them as contractors under U.S. labor law, effectively avoiding any requirements to pay employment taxes or provide health care and other fringe benefits.

“It’s the idea that you can get them to do what you want — essentially manage them — by sending them algorithm-based information about their performance and nudging them into using it to perform better,” Peter Cappelli writes in the Human Resource Executive blog. “What’s clever about all algorithmic management is that it is about the only way to influence workers on these platforms and keep them in contractor status.”

Alex Rosenblat, author of “Uberland: How Algorithms are Rewriting the Rules of Work,” says the company essentially has replaced human supervision with oversight through algorithms. The result for workers is the absence of the need to interact with a human boss, but there’s also more supervision – and evaluation — on the minutiae of job performance.

“An algorithmic manager can intervene in much more granular ways in how you behave, like when you break too harshly or accelerate too quickly, or when a passenger claims that you’re drunk,” Rosenblat says. “Those have immediate consequences on your work, and that type of on-the-spot engagement and intervention shows us that, actually, platforms can shape the choices we make even if they don’t appear to be curating our experience in any obvious way, like an editor would.”

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