For Lower-Paid Workers, the Robot Overlords Have Arrived
For Lower-Paid Workers, the Robot Overlords Have Arrived
Software and algorithms are used to screen, hire, assign
and now terminate workers
By Greg Ip Updated May 1, 2019 1:57 p.m. ET
It’s time to stop worrying that robots will take our
jobs—and start worrying that they will decide who gets jobs.
Millions of low-paid workers’ lives are increasingly
governed by software and algorithms. This was starkly illustrated by a report
last week that Amazon.com tracks the productivity of its employees and
regularly fires those who underperform, with little human intervention.
“Amazon’s system tracks the rates of each individual
associate’s productivity and automatically generates any warnings or
terminations regarding quality or productivity without input from supervisors,”
a law firm representing Amazon said in a letter to the National Labor Relations
Board, as first reported by technology news site The Verge. Amazon was
responding to a complaint that it had fired an employee from a Baltimore
fulfillment center for federally protected activity, which could include union
organizing. Amazon said the employee was fired for failing to meet productivity
targets.
Perhaps it was only a matter of time before software was
used to fire people. After all, it already screens resumes, recommends job
applicants, schedules shifts and assigns projects. In the workplace,
“sophisticated technology to track worker productivity on a minute-by-minute or
even second-by-second basis is incredibly pervasive,” says Ian Larkin, a
business professor at the University of California at Los Angeles specializing
in human resources.
Industrial laundry services track how many seconds it
takes to press a laundered shirt; on-board computers track truckers’ speed,
gear changes and engine revolutions per minute; and checkout terminals at major
discount retailers report if the cashier is scanning items quickly enough to
meet a preset goal. In all these cases, results are shared in real time with
the employee, and used to determine who is terminated, says Mr. Larkin.
Of course, weeding out underperforming employees is a
basic function of management. General Electric Co.’s former chief executive
Jack Welch regularly culled the company’s underperformers. “In banking and
management consulting it is standard to exit about 20% of employees a year,
even in good times, using ‘rank and yank’ systems,” says Nick Bloom, an
economist at Stanford University specializing in management.
For employees of General Electric, Goldman Sachs Group
Inc. and McKinsey & Co., that risk is more than compensated for by the
reward of stimulating and challenging work and handsome paychecks. The
risk-reward trade-off in industrial laundries, fulfillment centers and discount
stores is not nearly so enticing: the work is repetitive and the pay is low.
Those who aren’t weeded out one year may be the next if the company raises its
productivity targets. Indeed, wage inequality doesn’t fully capture how unequal
work has become: enjoyable and secure at the top, monotonous and insecure at
the bottom.
At fulfillment centers, employees locate, scan and box
all the items in an order. Amazon’s “Associate Development and Performance
Tracker,” or Adapt, tracks how each employee performs on these steps against
externally-established benchmarks and warns employees when they are falling
short.
Amazon employees have complained of being monitored
continuously—even having bathroom breaks measured—and being held to ever-rising
productivity benchmarks. There is no public data to determine if such
complaints are more or less common at Amazon than its peers. The company says
about 300 employees—roughly 10% of the Baltimore center’s employment level—were
terminated for productivity reasons in the year before the law firm’s letter
was sent to the NLRB.
Mr. Larkin says 10% is not unusually high. Yet,
automating the discipline process, he says, “makes an already difficult job
seem even more inhuman and undesirable. Dealing with these tough situations is
one of the key roles of managers.”
According to Amazon, the Adapt system sends a termination
notice to a manager and human resources, who then meet with the employee to
outline their options—which includes an appeal—before a final termination is
given. “No one is terminated, coached or developed by a system,” a spokeswoman
said. “Managers make final decisions on all personnel matters. The [Adapt
system] simply tracks and ensures consistency of data and process across
hundreds of employees to ensure fairness.” The number of terminations has
decreased in the last two years at the Baltimore facility and across North
America, she said.
Companies use these systems because they work well for
them.
Mr. Bloom and his co-authors find that companies that more
aggressively hire, fire and monitor employees have faster productivity growth.
They also have wider gaps between the highest- and lowest-paid employees.
Computers also don’t succumb to the biases managers do.
Economists Mitchell Hoffman, Lisa Kahn and Danielle Li looked at how 15 firms
used a job-testing technology that tested applicants on computer and technical
skills, personality, cognitive skills, fit for the job and various job
scenarios. Drawing on past correlations, the algorithm ranked applicants as
having high, moderate or low potential. Their study found employees hired
against the software’s recommendation were below-average performers: “This
suggests that managers often overrule test recommendations because they are
biased or mistaken, not only because they have superior private information,”
they wrote.
Last fall Amazon raised its starting pay to $15 an hour,
several dollars more than what the brick-and-mortar stores being displaced by
Amazon pay. Performance tracking is how Amazon ensures employees are productive
enough to merit that salary. This also means that, while employees may
increasingly be supervised by technology, at least they’re not about to be
replaced by it.
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