Thread by @cesifoti

In 2014, the New York Times reported a story about Jannette Navarro, a mother of a 4-year-old working at a Starbucks. With only a few days notice, she would be asked to work until 11 pm & return at 4 am the next day, a practice known as "clopening." judgingmachines.com /1

Navarro’s unpredictable work schedule made her life incredibly complicated. But her schedule was not being prepared by a sadistic manager. It was made by an algorithm created by a company called Kronos, a vendor that Starbucks hired to optimize its labor force./2

Starbucks updated its practices immediately after the Times ran Navarro’s story. Yet, years later, practices such as "clopening" still prevail in the low-wage sectors of the US economy. This story illustrates two important aspects of the relationship between AI & work. /3

The first one is the idea of technological displacement (embodied in the fact that Navarro's schedule was managed by an algorithm). The second one was the idea of work precarization (the idea that technology can reduce the quality of work). /4

Recently, displacement fears revived with reports claiming that almost half of all jobs could be automated and that this change could be happening “ten times faster [than] and at 300 times the scale” of the Industrial Revolution./5

But while economists agree in general that technology is labor-saving, automation is not a synonym of displacement. Technology can also increase worker productivity. This, plus new complementarities, can increase aggregate demand & stimulate the need for more human work. /6

A classic example involves automatic teller machines or (ATMs). ATMs did not eliminate the job of human tellers as some feared. They lowered the cost of opening new bank branches, which together with other factors, contributed to new--but different--bank teller jobs. /7

Another example is the use of QR codes in restaurants in China. QR codes allow customers to order food and pay their bills on their phones. But this does not replace the need for human servers. It only automates part of their tasks, allowing them to focus on other things. /8

These examples show that automation often does not replace entire jobs because it replaces tasks. Hence, the question that we should be asking is not “Will a robot take my job?” but “How will jobs change with technology?” Here, economists have made a few predictions: /9

There is an apparent consensus that while changes in technology have important effects on labor in the short term, they do not appear to affect the need for labor in the long run. /10

But there is less consensus on the redistributive effects of technology. Some scholars anticipate an increased polarization of the labor force and increased inequality. Others, have reached the opposite conclusion. /11

The fact that technology will affect the future of work is undeniable. But still, we have a limited understanding of how people react to the impact of technology on jobs compared to other forces. /12

In How Humans Judge Machines (judgingmachines.com, chapter 5) we run several experiments comparing people’s reactions to displacement attributed to technology with displacement attributed to humans. /13

Consider the following scenario:
"A trucking company is looking to lower costs by bringing in [temporary foreign drivers/autonomous trucks]. This change reduces the company’s costs by 30 percent, but several local drivers lose their jobs. /14

When we look at how people reacted to displacement attributed to automation (red) and displacement attributed to foreigners (blue), we find that people are much more accepting of technological displacement. They find it less immoral, and are not so keen on banning it. /15

We also explored alternatives such as offshoring, outsourcing, and replacing older workers by younger workers. For the most part, the effects were the same./16

People were more accepting of labor displacement due to automation than due to other humans, although they were relatively more accepting of practices such as offshoring than to foreign workers with temporary visas. /17
(judgingmachines.com, chapter 5)

But why this may be the case? First, people may see competing against a machine designed to excel at a specific task as futile, but competing against other humans, even when they are younger or foreign, as always possible. /18

A second possibility is that the negative reactions against displacement by foreign workers are automatic responses to well-socialized “in-group versus out-group” biases. In the US, displacement by foreigners is a narrative with a well-established negative connotation. /19

Also, people may perceive displacement by foreigners and younger people as more imminent to them, especially if they or someone they know has experienced a similar situation. /20

People may also oppose cost reductions based on cheaper labor more strongly because they consider profiting from lower salaries to be more exploitative, and less acceptable than profiting from technology. /21

So what can we do about this? On the side with a stronger taste for regulation, we find people in favor of a robot tax (i.e., a tax on the profits of companies that use more robots). /22

The argument is that because most tax revenue comes from labor income, tax policies tacitly incentivize automation. But if automation does not cause unemployment, but simply shifts workers to different jobs, we cannot use this argument to justify a robot tax. /23

On the side arguing for more flexibility, we find proposals focused on removing barriers limiting the ability of workers to move between occupations, and limiting new business models from entering established sectors./24

If you are interested in the full story, exact references, and details about the methodology, read chapter 5 of How Humans Judge Machines. Available free at judgingmachines.com or for purchase in Amazon & other online retailers amazon.com/Humans-Judge-M… /END