Automation could make 12 million jobs redundant. Here’s who’s most at risk

Although a European study, likely more broadly applicable to Canada and other countries, something that advocates of current and higher levels of immigration to Canada understate or ignore:

Up to a third of job roles in Europe could be made redundant by automation over the next 20 years as companies battle to increase productivity and fill skills gaps created by an ageing population, according to Forrester. 

The tech analyst’s latest Future of Jobs Forecast estimates that as many as 12 million jobs could be lost to automation across Europe by 2040, primarily impacting workers in industries such as retail, food services, and leisure and hospitality.  

Mid-skill labour jobs that consist of simple, routine tasks are most at risk from automation, the report said. These roles make up 38% of the workforce in Germany, 34% of the workforce in France, and 31% of the workforce in the UK. 

In total, 49 million jobs in ‘Europe-5’ (France, Germany, Italy, Spain, and the UK) could potentially be automated, according to Forrester. This jeopardizes casual work, such as zero-hour contracts in the UK, and low-paid, part-time jobs where workers hold “little bargaining power”. 

A combination of pressures is prompting businesses to ramp up their investments in automation, particularly in countries where industry, construction and agriculture are big business. 

While small and medium enterprises (SMEs) with up to 50 workers capture two-thirds of European employment, their productivity lags that of larger corporations, according to Forrester. In manufacturing, for example, ‘microenterprises’ are 40% less productive than large companies. 

A five-year study of robot adoption at French manufacturing firms found that robots lowered production overheads by reducing labor costs by between 4% and 6%. 

Business leaders also see automation technology as a means of filling the gaps created by Europe’s ageing population, which Forrester describes as “a demographic time bomb.” By 2050, Europe will have 30 million fewer people of working age than in 2020, the analyst said. 

Productivity lost to the pandemic is seeing organizations look to machine processes to recoup efficiency, while industries that were already using automation to grow their revenues have invested even more heavily in the technology to increase service delivery and mitigate pandemic restrictions. 

“Lost productivity due to COVID-19 is forcing companies globally to automate manual processes and improve remote work,” said Michael O’Grady, principal forecast analyst at Forrester. 

“European organisations are also in a particularly strong position to embrace automation because of Europe’s declining working-age population and the high number of routine, low-skilled jobs that can be easily automated.” 

While many low-skilled and routine roles face being replaced by machine processes, nine million new jobs are forecast to be created in Europe by 2040 in emerging sectors like green energy and smart cities, Forrester said.

This means that, all told, only three million jobs will truly be ‘lost’ to automation by 2040 – the caveat being that people who lose jobs may not find new ones.

Business leaders outside of Europe are also exploring the role of automation in bridging skills shortages and speeding up processes in the enterprise. 

Polling of 500 C-suite executives and senior management personnel by automation platform UIPath found that 78% were likely to increase their investment in automation tools to help them address labor shortages. Business leaders are turning to automation because they are struggling to find new talent (74%). 

At the same time, 85% of survey respondents said incorporating automation and automation training into their organization would help them attract new talent and hold onto existing staff. Meanwhile, leaders said automation was already helping them to save time (71%), improve productivity (63%) and save money (59%). 

Academic forecasts of jobs that could be lost to automation vary wildly. The European Parliament’s 2021 ‘Digital automation and the future of work’ report found that estimates varied from as little as 9% to almost half (47%). 

“Machine-learning experts often drive this uncertainty,” said Forrester. 

“They imagine future computer capabilities without understanding enterprise technology adoption constraints and the cultural barriers within an organization that resist change.” 

Source: https://www.zdnet.com/article/automation-could-make-12-million-jobs-redundant-heres-whos-most-at-risk/?utm_campaign=David%20Akin%27s%20🇨🇦%20Roundup&utm_medium=email&utm_source=Revue%20newsletter

Canada is refusing more study permits. Is new AI technology to blame?

Given the high volumes (which immigration lawyers and consultants benefit from), expanded use of technology and templates inevitable and necessary, although thorough review and safeguards necessary.

Alternate narrative, given reporting on abuse and exploitation of international students and the program itself (The reality of life in Canada for international students), perhaps a system generating more refusals has merit:

Soheil Moghadam applied twice for a study permit for a postgraduate program in Canada, only to be refused with an explanation that read like a templated answer.

The immigration officer was “not satisfied that you will leave Canada at the end of your stay,” he was told.

After a third failed attempt, Moghadam, who already has a master’s degree in electronics engineering from Iran, challenged the refusal in court and the case was settled. He’s now studying energy management at the New York Institute of Technology in Vancouver.

His Canadian lawyer, Zeynab Ziaie, said that in the past couple of years, she has noticed a growing number of study permit refusals like Moghadam’s. The internal notes made by officers reveal only generic analyses based on cookie-cutter language and often have nothing to do with the particular evidence presented by the applicant.

“We’re seeing a lot of people that previously would have been accepted or have really what we consider as complete files with lots of evidence of financial support, lots of ties to their home country. These kinds of files are just being refused,” said Ziaie, who added that she has seen more than 100 of these refusals in her practice in the past two years.

It’s a Microsoft Excel-based system called Chinook. 

Its existence came to light during a court case involving Abigail Ocran, a woman from Ghana who was refused a study permit by the Immigration Department.

Government lawyers in that case filed an affidavit by Andie Daponte, director of international-network optimization and modernization, who detailed the working and application of Chinook.

That affidavit has created a buzz among those practising immigration law, who see the new system — the department’s transition to artificial intelligence — as a potential threat to quality decision making, and its arrival as the harbinger of more troubling AI technology that could transform how immigration decisions are made in this country.

All eyes are now on the pending decision of the Ocran case to see if and how the court will weigh in on the use of Chinook. 


Chinook was implemented in March 2018 to help the Immigration Department handle an exponential growth in cases within its existing, and antiquated, Global Case Management System (GCMS).

Between 2011 and 2019, before everything slowed down during the pandemic, the number of visitor visa applications skyrocketed by 109 per cent, with the caseload of applications for overseas work permits and study permits up by 147 per cent and 222 per cent, respectively.

In 2019 alone, Daponte said in his affidavit, Canada received almost 2.2 million applications from prospective visitors, in addition to 366,000 from people looking to work here and 431,500 from would-be international students.

Meanwhile, the department’s 17-year-old GCMS system, which requires officers to open multiple screens to download different information pertaining to an application, has not caught up. Each time decision-makers move from screen to screen they must wait for the system to load, causing significant delays in processing, especially in countries with limited network bandwidth.

Chinook was developed in-house and implemented “to enhance efficiency and consistency, and to reduce processing times,” Daponte said.

As a result, he said, migration offices have generally seen an increase of between five per cent and 35 per cent in the number of applications they have been able to process.

Here’s how Chinook works: an applicant’s information is extracted from the old system and populated in a spreadsheet, with each cell on the same row filled with data from that one applicant — such as name, age, purpose of visit, date of receipt of the application and previous travel history.

Each spreadsheet contains content from multiple applicants and is assigned to an officer to enable them to use “batch processes.”

After the assessment of an application is done, the officer will click on the decision column to prompt a pop-up window to record the decision, along with a notes generator if they’re giving reasons in the case of a refusal.

(An officer can refuse or approve an application, and sometimes hold it for further information.)

When done, decision-makers click a button labelled “Action List,” which organizes data for ease of transfer into the old system. It presents the decision, reasons for refusal if applicable, and any “risk indicators” or “local word flags” for each application.

The spreadsheets are deleted daily after the data transfer for privacy concerns.

While working on the spreadsheet, said Daponte, decision-makers continue to have access to paper applications or electronic documents and GCMS if needed.

“Chinook was built to save decision-makers time in querying GCMS for application information and to allow for the review of multiple applications,” Daponte noted.

However, critics are concerned that the way the system is set up may be guiding the officers toward certain conclusions, giving them the option of not reviewing all the material presented in each case, and that it effectively shields much of the decision making from real scrutiny.

According to Daponte’s court affidavit, the notes generator presents standard language that immigration officers may select, review and modify to fit the circumstances of an application in preparing reasons for refusal. The function is there to “assist them in the creation of reasons.”

Ziaie believes that explains the templated reasons for refusals she’s been seeing.

“These officers are looking at a spreadsheet of potentially 100 different applicants. And those names don’t mean anything to the officers. You could mix up rows. You could easily make errors,” said the Toronto lawyer.

“There’s no way to go back and check that because these decisions end up with very similar notes that are generated right when they’re refused. So my concern is about accountability. Every time we have a decision, it has to make sense. We don’t know if they make mistakes.”

That’s why she and other lawyers worry the surge of study permit refusals is linked to the implementation of Chinook. 

In fact, that question was put to Daponte during the cross-examination in the Ocran case by the Ghanaian student’s lawyer, Edos Omorotionmwan.

Immigration data obtained by Omorotionmwan showed the refusal rate of student permit applications had gone from 31 per cent in 2016 to 34 per cent in 2018, the year Chinook was launched. The trend continued in 2019 to 40 per cent and reached 53 per cent last year.

“Is there a system within the Chinook software requiring some oversight function where there is some other person to review what a visa officer has come up with before that decision is handed over to the applicants?” asked Omorotionmwan.

“Within Chinook, no,” replied Daponte, who also said there’s no mechanism within this platform to track if an officer has reviewed all the support documents and information pertaining to an applicant’s file in the GCMS data.


“This idea of using portals and technology to speed up the way things are done is the reality of the future,” said Vancouver-based immigration lawyer Will Tao, who has tracked the uses of Chinook and blogged about it.

“My concern as an advocate is: who did this reality negatively impact and what systems does it continue to uphold?”

Tao said the way the row of personal information is selected and set out in the Chinook spreadsheet “disincentivizes” officers to go into the actual application materials and support documents out of convenience.

“And then the officers are supposed to use those notes generators to justify their reasoning and not go into some of the details that you would like to see to reflect that they actually reviewed the facts of the case. The biggest problem I have is that this system has had very limited oversight,” he said.

“It makes it easier to refuse because you don’t have to look at all the facts. You don’t have to go through a deep, thoughtful analysis. You have a refusal notes generator that you can apply without having read the detailed study plans and financial documents.”

He points to Chinook’s built-in function that flags “risk factors” — such as an applicant’s occupation and intended employer’s information — for inconsistency in an application, as well as “local flag words” to triage and ensure priority processing of time-sensitive applications to attend a wedding or a funeral.

Those very same flag words used in the spreadsheet can also be misused to mark a particular group of applicants based on their personal profiles and pick them out for refusals, said Tao.

In 2019, in a case involving the revocation of citizenship to the Canadian-born sons of two Russian spies, the Supreme Court of Canada made a landmark ruling that helps guide judges to review the decisions of immigration officials.

In the unanimous judgment, Canada’s highest court ruled it would be “unacceptable for an administrative decision maker to provide an affected party formal reasons that fail to justify its decision, but nevertheless expect that its decision would be upheld on the basis of internal records that were not available to that party.”

Tao said he’s closely watching how the Ocran decision is going to shed light on the application of Chinook in the wake of that Supreme Court of Canada ruling over the reasonableness standard.

“Obviously, a lot of these applications have critical points that they get refused on and with the reasons being template and standard, it’s hard for reviewers to understand how that came to be,” he said.

In a response to the Star’s inquiry about the concerns raised about Chinook, the Immigration Department said the tool is simply to streamline the administrative steps that would otherwise be required in the processing of applications to improve efficiency.

“Decision makers are required to review all applications and render their decisions based on the information presented before them,” said spokesperson Nancy Caron.

“Chinook does not fundamentally change the way applications are processed, and it is always the officer that gives the rational for the decisions and not the Chinook tool.”

For immigration lawyer Mario Bellissimo, Chinook is another step in the Immigration Department’s move toward digitalization and modernization.

Ottawa has been using machine learning technology since 2018 to triage temporary resident visa applications from China and India, using a “set of rules derived from thousands of past officer decisions” then deployed by the technology to classify applications into high, medium and low complexity.

Cases identified as low complexity and low risk automatically receive positive eligibility decisions, allowing officers to review these files exclusively on the basis of admissibility. This enables officers to spend more time scrutinizing the more complex files.

Chinook, said Bellissimo, has gone beyond the triage. He contends it facilitates the decision-making process by officers.

The use of templated responses from the notes generator makes the refusal reasons “devoid of meaning,” he noted.

“Eventually, do you see age discriminators put into place for study permits when anyone over the age of 30 is all automatically streamed to a different tier because they are less likely bona fide students? This is the type of stuff we need to know,” Bellissimo explained.

“When they’re just pulling standard refusal reasons and just slapping it in, then those decisions become more difficult to understand and more difficult to challenge. Who made the decision? Was technology used? And that becomes a problem.”

He said immigration officials need to be accountable and transparent to applicants about the use of these technologies before they are rolled out, not after they become an issue.

Petra Molnar, a Canadian expert specializing in migration and technology, said automated decision-making and artificial intelligence tools are difficult to scrutinize because they are often very opaque, including how they are developed and deployed and what review mechanisms, if any, exist once they are in use.

“Decisions in the immigration and refugee context have lifelong and life-altering ramifications. People have the right to know what types of tools are being used against them and how they work, so that we can meaningfully challenge these types of systems.”

Ziaie, the lawyer, said she understands the tremendous pressure on front-line immigration officers, but if charging a higher application fee — a study permit application now costs $150 — can help improve the service and quality of decisions, then that should be implemented.

“They should allocate a fair amount of that revenue toward trying to hire more people, train their officers better and give them more time to review the files so they actually do get a better success rate,” she said. “By that, I mean fewer files going to Federal Court.”

As a study permit applicant, Moghadam said it’s frustrating not to understand how an immigration officer reaches a refusal decision because so much is at stake for the applicant.

It took him two extra years to finally obtain his study permit and pursue an education in Canada, let alone the additional application fees and hefty legal costs.

“Your life is put on hold and your future is uncertain,” said the 39-year-old, who had a decade of work experience in engineering for both Iranian and international companies.

“There’s the time, the costs, the stress and the anxiety.”

Source: https://www.thestar.com/news/canada/2021/11/15/canada-is-refusing-more-study-permits-is-new-ai-technology-to-blame.html

Rise of the Robots Speeds Up in Pandemic With U.S. Labor Scarce

Of note to Canadian policy makers as well given this trend will cross the border and needs to be taken into account in immigration policy:

American workers are hoping that the tight pandemic labor market will translate into better pay. It might just mean robots take their jobs instead.

Labor shortages and rising wages are pushing U.S. business to invest in automation. A recent Federal Reserve survey of chief financial officers found that at firms with difficulty hiring, one-third are implementing or exploring automation to replace workers. In earnings calls over the past month, executives from a range of businesses confirmed the trend.

Domino’s Pizza Inc. is “putting in place equipment and technology that reduce the amount of labor that is required to produce our dough balls,” said Chief Executive Officer Ritch Allison.
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Mark Coffey, a group vice president at Hormel Foods Corp., said the maker of Spam spread and Skippy peanut butter is “ramping up our investments in automation” because of the “tight labor supply.”

The mechanizing of mundane tasks has been underway for generations. It’s made remarkable progress in the past decade: The number of industrial robots installed in the world’s factories more than doubled in that time, to about 3 million. Automation has been spreading into service businesses too.

The U.S. has lagged behind other economies, especially Asian ones, but the pandemic might trigger some catching up. With some 10.4 million open positions as of August, and record numbers of Americans quitting their jobs, the difficulty of finding staff is adding new incentives.

Ametek Inc. makes automation equipment for industrial firms, like motion trackers that are used from steel and lumber mills to packaging systems. Chief Executive Officer David A. Zapico says that part of the company is “firing on all cylinders.” That’s because “people want to remove labor from the processes,” he said on an earnings call. “In some places, you can’t hire labor.”

Unions have long seen automation as a threat. At U.S. ports, which lag their global peers in technology and are currently at the center of a major supply-chain crisis, the International Longshoremen’s Association has vowed to fight it.

Companies that say they want to automate “have one goal in mind: to eliminate your job, and put more money in their pockets,” ILA President Harold Daggett said in a video message to a June conference. “We’re going to fight this for 100 years.”

Some economists have warned that automation could make America’s income and wealth gaps worse.

“If it continues, labor demand will grow slowly, inequality will increase, and the prospects for many low-education workers will not be very good,” says Daron Acemoglu, a professor at the Massachusetts Institute of Technology, who testified Wednesday at a Senate hearing on the issue.

That’s not an inevitable outcome, Acemoglu says: Scientific knowhow could be used “to develop technologies that are more complementary to workers.” But, with research largely dominated by a handful of giant firms that spend the most money on it, “this is not the direction the technology is going currently.”

Knightscope makes security robots that look a bit like R2-D2 from Star Wars, and can patrol sites such as factory perimeters. The company says it’s attracting new clients who are having trouble hiring workers to keep watch. Its robots cost from $3.50 to $7.50 an hour, according to Chief Client Officer Stacy Stephens, and can be installed a month after signing a contract.

One new customer is the Los Angeles International Airport, one of the busiest in the U.S. Soon, Knightscope robots will be monitoring some of its parking lots.

They are “supplementing what we have in place and are not replacing any human services,” said Heath Montgomery, the airport’s director of public relations. “It’s another way we are providing exceptional guest experiences.”

Source: Rise of the Robots Speeds Up in Pandemic With U.S. Labor Scarce

Misattributed blame? Attitudes toward globalization in the age of automation

Interesting study and findings:

Many, especially low-skilled workers, blame globalization for their economic woes. Robots and machines, which have led to job market polarization, rising income inequality, and labor displacement, are often viewed much more forgivingly. This paper argues that citizens have a tendency to misattribute blame for economic dislocations toward immigrants and workers abroad, while discounting the effects of technology. Using the 2016 American National Elections Studies, a nationally representative survey, I show that workers facing higher risks of automation are more likely to oppose free trade agreements and favor immigration restrictions, even controlling for standard explanations for these attitudes. Although pocket-book concerns do influence attitudes toward globalization, this study calls into question the standard assumption that individuals understand and can correctly identify the sources of their economic anxieties. Accelerated automation may have intensified attempts to resist globalization.

Source: https://www.cambridge.org/core/journals/political-science-research-and-methods/article/misattributed-blame-attitudes-toward-globalization-in-the-age-of-automation/29B08295CEAC4A4A89991E064D0284FF

Pandemic Wave of Automation May Be Bad News for Workers

Interesting trend affecting lower skilled workers, one that will likely affect Canada and that needs to be taken into account by the immigration program in terms of levels and mix, particularly those in retail, hospitality, warehousing and manufacturing. This may also increase the productivity gap between Canada and the USA:

When Kroger customers in Cincinnati shop online these days, their groceries may be picked out not by a worker in their local supermarket but by a robot in a nearby warehouse.

Gamers at Dave & Buster’s in Dallas who want pretzel dogs can order and pay from their phones — no need to flag down a waiter.

And in the drive-through lane at Checkers near Atlanta, requests for Big Buford burgers and Mother Cruncher chicken sandwiches may be fielded not by a cashier in a headset, but by a voice-recognition algorithm.

An increase in automation, especially in service industries, may prove to be an economic legacy of the pandemic. Businesses from factories to fast-food outlets to hotels turned to technology last year to keep operations running amid social distancing requirements and contagion fears. Now the outbreak is ebbing in the United States, but the difficulty in hiring workers — at least at the wages that employers are used to paying — is providing new momentum for automation.

Technological investments that were made in response to the crisis may contribute to a post-pandemic productivity boom, allowing for higher wages and faster growth. But some economists say the latest wave of automation could eliminate jobs and erode bargaining power, particularly for the lowest-paid workers, in a lasting way.

“Once a job is automated, it’s pretty hard to turn back,” said Casey Warman, an economist at Dalhousie University in Nova Scotia who has studied automation in the pandemic.

The trend toward automation predates the pandemic, but it has accelerated at what is proving to be a critical moment. The rapid reopening of the economy has led to a surge in demand for waiters, hotel maids, retail sales clerks and other workers in service industries that had cut their staffs. At the same time, government benefits have allowed many people to be selective in the jobs they take. Together, those forces have given low-wage workers a rare moment of leverage, leading to higher pay, more generous benefits and other perks.

Automation threatens to tip the advantage back toward employers, potentially eroding those gains. A working paper published by the International Monetary Fund this year predicted that pandemic-induced automation would increase inequality in coming years, not just in the United States but around the world.

“Six months ago, all these workers were essential,” said Marc Perrone, president of the United Food and Commercial Workers, a union representing grocery workers. “Everyone was calling them heroes. Now, they’re trying to figure out how to get rid of them.”

Checkers, like many fast-food restaurants, experienced a jump in sales when the pandemic shut down most in-person dining. But finding workers to meet that demand proved difficult — so much so that Shana Gonzales, a Checkers franchisee in the Atlanta area, found herself back behind the cash register three decades after she started working part time at Taco Bell while in high school.

“We really felt like there has to be another solution,” she said.

So Ms. Gonzales contacted Valyant AI, a Colorado-based start-up that makes voice recognition systems for restaurants. In December, after weeks of setup and testing, Valyant’s technology began taking orders at one of Ms. Gonzales’s drive-through lanes. Now customers are greeted by an automated voice designed to understand their orders — including modifications and special requests — suggest add-ons like fries or a shake, and feed the information directly to the kitchen and the cashier.

The rollout has been successful enough that Ms. Gonzales is getting ready to expand the system to her three other restaurants.

“We’ll look back and say why didn’t we do this sooner,” she said.

The push toward automation goes far beyond the restaurant sector. Hotels, retailersmanufacturers and other businesses have all accelerated technological investments. In a survey of nearly 300 global companies by the World Economic Forum last year, 43 percent of businesses said they expected to reduce their work forces through new uses of technology.

Some economists see the increased investment as encouraging. For much of the past two decades, the U.S. economy has struggled with weak productivity growth, leaving workers and stockholders to compete over their share of the income — a game that workers tended to lose. Automation may harm specific workers, but if it makes the economy more productive, that could be good for workers as a whole, said Katy George, a senior partner at McKinsey, the consulting firm.

She cited the example of a client in manufacturing who had been pushing his company for years to embrace augmented-reality technology in its factories. The pandemic finally helped him win the battle: With air travel off limits, the technology was the only way to bring in an expert to help troubleshoot issues at a remote plant.

“For the first time, we’re seeing that these technologies are both increasing productivity, lowering cost, but they’re also increasing flexibility,” she said. “We’re starting to see real momentum building, which is great news for the world, frankly.”

Other economists are less sanguine. Daron Acemoglu of the Massachusetts Institute of Technology said that many of the technological investments had just replaced human labor without adding much to overall productivity.

In a recent working paper, Professor Acemoglu and a colleague concluded that “a significant portion of the rise in U.S. wage inequality over the last four decades has been driven by automation” — and he said that trend had almost certainly accelerated in the pandemic.

“If we automated less, we would not actually have generated that much less output but we would have had a very different trajectory for inequality,” Professor Acemoglu said.

Ms. Gonzales, the Checkers franchisee, isn’t looking to cut jobs. She said she would hire 30 people if she could find them. And she has raised hourly pay to about $10 for entry-level workers, from about $9 before the pandemic. Technology, she said, is easing pressure on workers and speeding up service when restaurants are chronically understaffed.

“Our approach is, this is an assistant for you,” she said. “This allows our employee to really focus” on customers.

Ms. Gonzales acknowledged she could fully staff her restaurants if she offered $14 to $15 an hour to attract workers. But doing so, she said, would force her to raise prices so much that she would lose sales — and automation allows her to take another course.

Rob Carpenter, Valyant’s chief executive, noted that at most restaurants, taking drive-through orders is only part of an employee’s responsibilities. Automating that task doesn’t eliminate a job; it makes the job more manageable.

“We’re not talking about automating an entire position,” he said. “It’s just one task within the restaurant, and it’s gnarly, one of the least desirable tasks.”

But technology doesn’t have to take over all aspects of a job to leave workers worse off. If automation allows a restaurant that used to require 10 employees a shift to operate with eight or nine, that will mean fewer jobs in the long run. And even in the short term, the technology could erode workers’ bargaining power.

“Often you displace enough of the tasks in an occupation and suddenly that occupation is no more,” Professor Acemoglu said. “It might kick me out of a job, or if I keep my job I’ll get lower wages.”

At some businesses, automation is already affecting the number and type of jobs available. Meltwich, a restaurant chain that started in Canada and is expanding into the United States, has embraced a range of technologies to cut back on labor costs. Its grills no longer require someone to flip burgers — they grill both sides at once, and need little more than the press of a button.

“You can pull a less-skilled worker in and have them adapt to our system much easier,” said Ryan Hillis, a Meltwich vice president. “It certainly widens the scope of who you can have behind that grill.”

With more advanced kitchen equipment, software that allows online orders to flow directly to the restaurant and other technological advances, Meltwich needs only two to three workers on a shift, rather than three or four, Mr. Hillis said.

Such changes, multiplied across thousands of businesses in dozens of industries, could significantly change workers’ prospects. Professor Warman, the Canadian economist, said technologies developed for one purpose tend to spread to similar tasks, which could make it hard for workers harmed by automation to shift to another occupation or industry.

“If a whole sector of labor is hit, then where do those workers go?” Professor Warman said. Women, and to a lesser degree people of color, are likely to be disproportionately affected, he added.

The grocery business has long been a source of steady, often unionized jobs for people without a college degree. But technology is changing the sector. Self-checkout lanes have reduced the number of cashiers; many stores have simple robots to patrol aisles for spills and check inventory; and warehouses have become increasingly automated. Kroger in April opened a 375,000-square-foot warehouse with more than 1,000 robots that bag groceries for delivery customers. The company is even experimenting with delivering groceries by drone.

Other companies in the industry are doing the same. Jennifer Brogan, a spokeswoman for Stop & Shop, a grocery chain based in New England, said that technology allowed the company to better serve customers — and that it was a competitive necessity.

“Competitors and other players in the retail space are developing technologies and partnerships to reduce their costs and offer improved service and value for customers,” she said. “Stop & Shop needs to do the same.”

In 2011, Patrice Thomas took a part-time job in the deli at a Stop & Shop in Norwich, Conn. A decade later, he manages the store’s prepared foods department, earning around $40,000 a year.

Mr. Thomas, 32, said that he wasn’t concerned about being replaced by a robot anytime soon, and that he welcomed technologies making him more productive — like more powerful ovens for rotisserie chickens and blast chillers that quickly cool items that must be stored cold.

But he worries about other technologies — like automated meat slicers — that seem to enable grocers to rely on less experienced, lower-paid workers and make it harder to build a career in the industry.

“The business model we seem to be following is we’re pushing toward automation and we’re not investing equally in the worker,” he said. “Today it’s, ‘We want to get these robots in here to replace you because we feel like you’re overpaid and we can get this kid in there and all he has to do is push this button.’”

Source: https://www.nytimes.com/2021/07/03/business/economy/automation-workers-robots-pandemic.html?action=click&module=Top%20Stories&pgtype=Homepage

The Robots Are Coming for Phil in Accounting

Implications for many white collar workers, including in government given the nature of repetitive operational work:

The robots are coming. Not to kill you with lasers, or beat you in chess, or even to ferry you around town in a driverless Uber.

These robots are here to merge purchase orders into columns J and K of next quarter’s revenue forecast, and transfer customer data from the invoicing software to the Oracle database. They are unassuming software programs with names like “Auxiliobits — DataTable To Json String,” and they are becoming the star employees at many American companies.

Some of these tools are simple apps, downloaded from online stores and installed by corporate I.T. departments, that do the dull-but-critical tasks that someone named Phil in Accounting used to do: reconciling bank statements, approving expense reports, reviewing tax forms. Others are expensive, custom-built software packages, armed with more sophisticated types of artificial intelligence, that are capable of doing the kinds of cognitive work that once required teams of highly-paid humans.

White-collar workers, armed with college degrees and specialized training, once felt relatively safe from automation. But recent advances in A.I. and machine learning have created algorithms capable of outperforming doctorslawyers and bankers at certain parts of their jobs. And as bots learn to do higher-value tasks, they are climbing the corporate ladder.

The trend — quietly building for years, but accelerating to warp speed since the pandemic — goes by the sleepy moniker “robotic process automation.” And it is transforming workplaces at a pace that few outsiders appreciate. Nearly 8 in 10 corporate executives surveyed by Deloitte last year said they had implemented some form of R.P.A. Another 16 percent said they planned to do so within three years.

Most of this automation is being done by companies you’ve probably never heard of. UiPath, the largest stand-alone automation firm, is valued at $35 billion — roughly the size of eBay — and is slated to go public later this year. Other companies like Automation Anywhere and Blue Prism, which have Fortune 500 companies like Coca-Cola and Walgreens Boots Alliance as clients, are also enjoying breakneck growth, and tech giants like Microsoft have recently introduced their own automation products to get in on the action.

Executives generally spin these bots as being good for everyone, “streamlining operations” while “liberating workers” from mundane and repetitive tasks. But they are also liberating plenty of people from their jobs. Independent experts say that major corporate R.P.A. initiatives have been followed by rounds of layoffs, and that cutting costs, not improving workplace conditions, is usually the driving factor behind the decision to automate. 

Craig Le Clair, an analyst with Forrester Research who studies the corporate automation market, said that for executives, much of the appeal of R.P.A. bots is that they are cheap, easy to use and compatible with their existing back-end systems. He said that companies often rely on them to juice short-term profits, rather than embarking on more expensive tech upgrades that might take years to pay for themselves.

“It’s not a moonshot project like a lot of A.I., so companies are doing it like crazy,” Mr. Le Clair said. “With R.P.A., you can build a bot that costs $10,000 a year and take out two to four humans.”

Covid-19 has led some companies to turn to automation to deal with growing demand, closed offices, or budget constraints. But for other companies, the pandemic has provided cover for executives to implement ambitious automation plans they dreamed up long ago.

“Automation is more politically acceptable now,” said Raul Vega, the chief executive of Auxis, a firm that helps companies automate their operations.

Before the pandemic, Mr. Vega said, some executives turned down offers to automate their call centers, or shrink their finance departments, because they worried about scaring their remaining workers or provoking a backlash like the one that followed the outsourcing boom of the 1990s, when C.E.O.s became villains for sending jobs to Bangalore and Shenzhen.

But those concerns matter less now, with millions of people already out of work and many businesses struggling to stay afloat.

Now, Mr. Vega said, “they don’t really care, they’re just going to do what’s right for their business,” Mr. Vega said.

Sales of automation software are expected to rise by 20 percent this year, after increasing by 12 percent last year, according to the research firm Gartner. And the consulting firm McKinsey, which predicted before the pandemic that 37 million U.S. workers would be displaced by automation by 2030, recently increased its projection to 45 million.

A white-collar wake-up call

Not all bots are the job-destroying kind. Holly Uhl, a technology manager at State Auto Insurance Companies, said that her firm has used automation to do 173,000 hours’ worth of work in areas like underwriting and human resources without laying anyone off.

“People are concerned that there’s a possibility of losing their jobs, or not having anything to do,” she said. “But once we have a bot in the area, and people see how automation is applied, they’re truly thrilled that they don’t have to do that work anymore.”

As bots become capable of complex decision-making, rather than doing single repetitive tasks, their disruptive potential is growing.

Recent studies by researchers at Stanford University and the Brookings Institution compared the text of job listings with the wording of A.I.-related patents, looking for phrases like “make prediction” and “generate recommendation” that appeared in both. They found that the groups with the highest exposure to A.I. were better-paid, better-educated workers in technical and supervisory roles, with men, white and Asian-American workers, and midcareer professionals being some of the most endangered. Workers with bachelor’s or graduate degrees were nearly four times as exposed to A.I. risk as those with just a high school degree, the researchers found, and residents of high-tech cities like Seattle and Salt Lake City were more vulnerable than workers in smaller, more rural communities.

“A lot of professional work combines some element of routine information processing with an element of judgment and discretion,” said David Autor, an economist at M.I.T. who studies the labor effects of automation. “That’s where software has always fallen short. But with A.I., that type of work is much more in the kill path.”

Many of those vulnerable workers don’t see this coming, in part because the effects of white-collar automation are often couched in jargon and euphemism. On their websites, R.P.A. firms promote glowing testimonials from their customers, often glossing over the parts that involve actual humans.

“Sprint Automates 50 Business Processes In Just Six Months.” (Possible translation: Sprint replaced 300 people in the billing department.)

“Dai-ichi Life Insurance Saves 132,000 Hours Annually” (Bye-bye, claims adjusters.)

“600% Productivity Gain for Credit Reporting Giant with R.P.A.”(Don’t let the door hit you, data analysts.)

Jason Kingdon, the chief executive of the R.P.A. firm Blue Prism, speaks in the softened vernacular of displacement too. He refers to his company’s bots as “digital workers,” and he explained that the economic shock of the pandemic had “massively raised awareness” among executives about the variety of work that no longer requires human involvement.

“We think any business process can be automated,” he said.

Mr. Kingdon tells business leaders that between half and two-thirds of all the tasks currently being done at their companies can be done by machines. Ultimately, he sees a future in which humans will collaborate side-by-side with teams of digital employees, with plenty of work for everyone, although he conceded that the robots have certain natural advantages.

“A digital worker,” he said, “can be scaled in a vastly more flexible way.”

Humans have feared losing our jobs to machines for millennia. (In 350 BCE, Aristotle worried that self-playing harps would make musicians obsolete.) And yet, automation has never created mass unemployment, in part because technology has always generated new jobs to replace the ones it destroyed.

During the 19th and 20th centuries, some lamplighters and blacksmiths became obsolete, but more people were able to make a living as electricians and car dealers. And today’s A.I. optimists argue that while new technology may displace some workers, it will spur economic growth and create better, more fulfilling jobs, just as it has in the past.

But that is no guarantee, and there is growing evidence that this time may be different.

In a series of recent studies, Daron Acemoglu of M.I.T. and Pascual Restrepo of Boston University, two well-respected economists who have researched the history of automation, found that for most of the 20th century, the optimistic take on automation prevailed — on average, in industries that implemented automation, new tasks were created faster than old ones were destroyed.

Since the late 1980s, they found, the equation had flipped — tasks have been disappearing to automation faster than new ones are appearing.

This shift may be related to the popularity of what they call “so-so automation” — technology that is just barely good enough to replace human workers, but not good enough to create new jobs or make companies significantly more productive.

A common example of so-so automation is the grocery store self-checkout machine. These machines don’t cause customers to buy more groceries, or help them shop significantly faster — they simply allow store owners to staff slightly fewer employees on a shift. This simple, substitutive kind of automation, Mr. Acemoglu and Mr. Restrepo wrote, threatens not just individual workers, but the economy as a whole.

“The real danger for labor,” they wrote, “may come not from highly productive but from ‘so-so’ automation technologies that are just productive enough to be adopted and cause displacement.”

Only the most devoted Luddites would argue against automating any job, no matter how menial or dangerous. But not all automation is created equal, and much of the automation being done in white-collar workplaces today is the kind that may not help workers over the long run.

During past eras of technological change, governments and labor unions have stepped in to fight for automation-prone workers, or support them while they trained for new jobs. But this time, there is less in the way of help. Congress has rejected calls to fund federal worker retraining programs for years, and while some of the money in the $1.9 trillion Covid-19 relief bill Democrats hope to pass this week will go to laid-off and furloughed workers, none of it is specifically earmarked for job training programs that could help displaced workers get back on their feet.

Another key difference is that in the past, automation arrived gradually, factory machine by factory machine. But today’s white-collar automation is so sudden — and often, so deliberately obscured by management — that few workers have time to prepare.

“The rate of progression of this technology is faster than any previous automation,” said Mr. Le Clair, the Forrester analyst, who thinks we are closer to the beginning than the end of the corporate A.I. boom.

“We haven’t hit the exponential point of this stuff yet,” he added. “And when we do, it’s going to be dramatic.”

The corporate world’s automation fever isn’t purely about getting rid of workers. Executives have shareholders and boards to satisfy, and competitors to keep up with. And some automation does, in fact, lift all boats, making workers’ jobs better and more interesting while allowing companies to do more with less.

But as A.I. enters the corporate world, it is forcing workers at all levels to adapt, and focus on developing the kinds of distinctly human skills that machines can’t easily replicate.

Ellen Wengert, a former data processor at an Australian insurance firm, learned this lesson four years ago, when she arrived at work one day to find a bot-builder sitting in her seat.

The man, coincidentally an old classmate of hers, worked for a consulting firm that specialized in R.P.A. He explained that he’d been hired to automate her job, which mostly involved moving customer data from one database to another. He then asked her to, essentially, train her own replacement — teaching him how to do the steps involved in her job so that he, in turn, could program a bot to do the same thing.

Ms. Wengert wasn’t exactly surprised. She’d known that her job was straightforward and repetitive, making it low-hanging fruit for automation. But she was annoyed that her managers seemed so eager to hand it over to a machine.

“They were desperate to create this sense of excitement around automation,” she said. “Most of my colleagues got on board with that pretty readily, but I found it really jarring, to be feigning excitement about us all potentially losing our jobs.”

For Ms. Wengert, 27, the experience was a wake-up call. She had a college degree and was early in her career. But some of her colleagues had been happily doing the same job for years, and she worried that they would fall through the cracks.

“Even though these aren’t glamorous jobs, there are a lot of people doing them,” she said.

She left the insurance company after her contract ended. And she now works as a second-grade teacher — a job she says she sought out, in part, because it seemed harder to automate.

Source: https://www.nytimes.com/2021/03/06/business/the-robots-are-coming-for-phil-in-accounting.html

ICYMI: The Demographics of Automation in Canada: Who Is at Risk?

The executive summary, highlighting that the most vulnerable share the same characteristics as those having poor economic outcomes in the past. I wonder, however, whether the discovery of who are truly essential workers during COVID-19, would affect their conclusions:

The COVID-19 pandemic has exposed a new vulnerability among firms that rely on human labour. In order to comply with public health directives on physical distancing, many businesses have had to completely shut down their operations for months. Others remained functional thanks to teleworking, which many intend to prolong and even adopt permanently. As experts contemplate the long-term repercussions of the pandemic on the economy, many expect firms to ramp up their adoption of new technologies to better weather the post-pandemic recession and insulate themselves from future health crises.

Just a few years ago, policy-makers became concerned about the prospect of many job-related tasks being automated using advances in robotics and artificial intelligence and in particular about the projected job losses at the time. While we no longer expect entire jobs to disappear, new technologies may substantially transform jobs, forcing workers to adjust to new requirements and prompting governments to assist them in this process.

In this study, Statistics Canada researchers Marc Frenette and Kristyn Frank are breaking new ground by examining the demographic and employment characteristics of workers facing a high risk of job transformation due to automation. To assess the potential impact of a new wave of automation on vulnerable workers, policy-makers need to know not only what jobs are at risk but also who holds these jobs. For instance, while we know that previous waves of robotization replaced low-skilled workers and enhanced the work of those with high skills, this time around there are fears it is high-skilled workers who are at risk, given the rise of new algorithms that are increasingly proficient at accomplishing complex cognitive tasks.

Consistent with the findings of previous research, Frenette and Frank show that, overall, more than 10 percent of Canadian workers face a high risk of seeing their jobs transformed through automation – high risk being defined as a probability of 70 percent and higher. And close to 30 percent of workers face a 50-to-70 percent risk. What the authors underscore, however, is that the extent to which the automation risk varies is based on certain worker characteristics. For instance, more than a third of workers without a certificate, diploma or degree face a high risk of job transformation, compared with fewer than 4 percent for those with degrees. The probability of being at high risk also decreases significantly as employment income increases. Over a quarter of workers in the bottom decile of the income distribution are at high risk, whereas only 2 percent of workers in the top decile are. Also among the groups most exposed to job transformation are older workers (aged 55 and over), those with low literacy or numeracy proficiency, the part-time employed,  those working in small firms, and in manufacturing, where about 27 percent workers are at high risk. The authors find no significant differences in the risk of job transformation on the basis of gender, immigration status, having a disability or being unionized.

The results from this study stand in sharp contrast with many observers’ expectations that the new technologies could adversely affect workers previously seen as immune to automation. It suggests that the workers at high risk of job transformation due to automation, by and large, share the same characteristics as workers who have been susceptible to poor labour market outcomes in the past. Frenette and Frank say that more research is needed to better understand which characteristics can be interpreted as risk factors. Nevertheless, by shedding light on the differential effects of automation on particular segments of the workforce, their study contributes to labour market policy development going forward.

Source: https://irpp.org/research-studies/the-demographics-of-automation-in-canada-who-is-at-risk/

New Zealand: ‘Like swimming in crocodile waters’ – Immigration officials’ data analytics use

Of note. As always, one needs to ensure that AI systems are as free of bias as possible as well as remembering that human decision-making is also not perfect. But any large-scale immigration system will likely have to rely on AI in order to manage the workload:

Immigration officials are being accused of using data analytics and algorithms in visa processing – and leaving applicants in the dark about why they are being rejected.

One immigration adviser described how applicants unaware of risk profiling were like unwitting swimmers in crocodile infested waters.

The automatic ‘triage’ system places tourists, overseas students or immigrants into high, medium or low risk categories.

The factors which raise a red flag on high-risk applications are not made publicly available; Official Information Act requests are redacted on the grounds of international relations.

But an immigration manager has told RNZ that staff identify patterns, such as overstaying and asylum claim rates of certain nationalities or visa types, and feed that data into the triage system.

On a recent visit to a visa processing centre in Auckland, Immigration New Zealand assistant general manager Jeannie Melville acknowledged that it now ran an automated system that triages applications, but said it was humans who make the decisions.

“There is an automatic triage that’s done – but to be honest, the most important thing is the work that our immigration officers do in actually determining how the application should be processed,” she said.

“And we do have immigration officers that have the skills and the experience to be able to determine whether there are further risk factors or no risk factors in a particular application.

“The triage system is something that we work on all the time because as you would expect, things change all the time. And we try and make sure that it’s a dynamic system that takes into account a whole range of factors, whether that be things that have happened in the past or things that are going on at the present time.”

When asked what ‘things that have happened in the past’ might mean in the context of deciding what risk category an applicant would be assigned to, another manager filled the silence.

“Immigration outcomes, application outcomes, things that we measure – overstaying rates or asylum claim rates from certain sources,” she said. “Nationality or visa type patterns that may have trended, so we do some data analytics that feed into some of those business rules.”

Humans defer to machines – professor

Professor Colin Gavaghan, of Otago University, said studies on human interactions with technology suggested people found it hard to ignore computerised judgments.

“What they’ve found is if you’re not very, very careful, you get a kind of situation where the human tends just to defer to whatever the machine recommends,” said Prof Gavaghan, director of the New Zealand Law Foundation Centre for Law and Policy in Emerging Technologies.

“It’s very hard to stay in a position where you’re actually critiquing and making your own independent decision – humans who are going to get to see these cases, they’ll be told that the machine, the system has already flagged them up as being high risk.

“It’s hard not to think that that will influence their decision. The idea they’re going to make a completely fresh call on those cases, I think, if we’re not careful, could be a bit unrealistic.”

Oversight and transparency were needed to check the accuracy of calls made by the algorithmic system and to ensure people could challenge decisions, he added.

Best practice guidelines tended to be high level and vague, he added.

“There’s also questions and concerns about bias,” he said. “It can be biased because the training data that’s been used to prepare it is itself the product of user bias decisions – if you have a body of data that’s been used to train the system that’s informed by let’s say, for the sake of argument, racist assumptions about particular groups, then that’s going to come through in the system’s recommendations as well.

“We haven’t had what we would like to see, which is one body with responsibility to look across all of government and all of these uses.”

The concerns follow questions around another Immigration New Zealand programme in 2018 which was used to prioritise deportations.

A compliance manager told RNZ it was using data, including nationality, of former immigrants to determine which future overstayers to target.

It subsequently denied that nationality was one of the factors but axed the programme.

Don’t make assumptions on raw data – immigration adviser

Immigration adviser Katy Armstrong said Immigration New Zealand had to fight its own ‘jaundice’ that was based on profiling and presumptions.

“Just because you’re a 23-year-old, let’s say, Brazilian coming in, wanting to have a holiday experience in New Zealand, doesn’t make you an enemy of the state.

“And you’re being lumped in maybe with a whole bunch of statistics that might say that young male Brazilians have a particular pattern of behaviour.

“So you then have to prove a negative against you, but you’re not being told transparently what that negative is.”

It would be unacceptable if the police were arresting people based on the previous offending rates of a certain nationality and immigration rules were also based on fairness and natural justice, she said.

“That means not discriminating, not being presumptuous about the way people may behave just purely based on assumptions from raw data,” she said.

“And that’s the area of real concern. If you have profiling and an unsophisticated workforce, with an organisation that is constantly in churn, with people coming on board to make decisions about people’s lives with very little training, then what do you end up with?

“Well, I can tell you – you end up with decisions that are basically unfair, and often biased.

“I think people go in very trusting of the system and not realising that there is this almighty wall between them and a visa over issues that they would have no inkling about.

“And then they get turned down, they don’t even give you a chance very often to respond to any doubts that immigration might have around you.

“People come and say: ‘I got declined’ and you look at it and you think ‘oh my God, it was like they literally went swimming in the crocodile waters without any protection’.”

Source: ‘Like swimming in crocodile waters’ – Immigration officials’ data analytics use

Will Your Job Still Exist In 2030?

More on the expected impact of automation and AI:

Automation is already here. Robots helped build your car and pack your latest online shopping order. A chatbot might help you figure out your credit card balance. A computer program might scan and process your résumé when you apply for work.

What will work in America look like a decade from now? A team of economists at the McKinsey Global Institute set out to figure it out in a new report out Thursday.

The research finds automation widening the gap between urban and rural areas and dramatically affecting people who didn’t go to college or didn’t finish high school. It also projects some occupations poised for massive growth or growing enough to offset displaced jobs.

Below are some of the key takeaways from McKinsey’s forecast.

Most jobs will change; some will decline“Intelligent machines are going to become more prevalent in every business. All of our jobs are going to change,” said Susan Lund, co-author of the report. Almost 40% of U.S. jobs are in occupations that are likely to shrink — though not necessarily disappear — by 2030, the researchers found.

Employing almost 21 million Americans, office support is by far the most common U.S. occupation that’s most at risk of losing jobs to digital services, according to McKinsey. Food service is another heavily affected category, as hotel, fast-food and other kitchens automate the work of cooks, dishwashers and others.

At the same time, “the economy is adding jobs that make use of new technologies,” McKinsey economists wrote. Those jobs include software developers and information security specialists — who are constantly in short supply — but also solar panel installers and wind turbine technicians.

Health care jobs, including hearing aid specialists and home health aides, will stay in high demand for the next decade, as baby boomers age. McKinsey also forecast growth for jobs that tap into human creativity or “socioemotional skills” or provide personal service for the wealthy, like interior designers, psychologists, massage therapists, dietitians and landscape architects.

In some occupations, even as jobs disappear, new ones might offset the losses. For example, digital assistants might replace counter attendants and clerks who help with rentals, but more workers might be needed to help shoppers in stores or staff distribution centers, McKinsey economists wrote.

Similarly, enough new jobs will be created in transportation or customer service and sales to offset ones lost by 2030.

Employers and communities could do more to match workers in waning fields to other compatible jobs with less risk of automation. For instance, 900,000 bookkeepers, accountants and auditing clerks nationwide might see their jobs phased out but could be retrained to become loan officers, claims adjusters or insurance underwriters, the McKinsey report said.

Automation is likely to continue widening the gap between job growth in urban and rural areas

By 2030, the majority of job growth may be concentrated in just 25 megacities and their peripheries, while large swaths of the country see slower job creation and even lose jobs, the researchers found. This gap has already widened in the past decade, as Federal Reserve Chairman Jerome Powell noted in his remarks on Wednesday.

Source: Will Your Job Still Exist In 2030?

The robot revolution will be worse for men

Interesting long read and analysis:

Demographics will determine who gets hit worst by automation. Policy will help curb the damage.

The robots will someday take our jobs. But not all our jobs, and we don’t really know how many. Nor do we understand which jobs will be eliminated and which will be transitioned into what some say will be better, less tedious work.

What we do know is that automation and artificial intelligence will affect Americans unevenly, according to data from McKinsey and the 2016 US Census that was analyzed by the Brookings think tank.

Young people — especially those in rural areas or who are underrepresented minorities — will have a greater likelihood of having their jobs replaced by automation. Meanwhile, older, more educated white people living in big cities are more likely to maintain their coveted positions, either because their jobs are irreplaceable or because they’re needed in new jobs alongside our robot overlords.

The Brookings study also warns that automation will exacerbate existing social inequalities along certain geographic and demographic lines, because it will likely eliminate many lower- and middle-skill jobs considered stepping stones to more advanced careers. These jobs losses will be in concentrated in rural areas, particularly the swath of America between the coasts.

However, at least in the case of gender, it’s the men, for once, who will be getting the short end of the stick. Jobs traditionally held by men have a higher “average automation potential” than those held by women, meaning that a greater share of those tasks could be automated with current technology, according to Brookings. That’s because the occupations men are more likely to hold tend to be more manual and more easily replaced by machines and artificial intelligence.

Of course, the real point here is that people of all stripes face employment disruption as new technologies are able to do many of our tasks faster, more efficiently, and more precisely than mere mortals. The implications of this unemployment upheaval are far-reaching and raise many questions: How will people transition to the jobs of the future? What will those jobs be? Is it possible to mitigate the polarizing effects automation will have on our already-stratified society of haves and have-nots?

A recent McKinsey report estimated that by 2030, up to one-third of work activities could be displaced by automation, meaning a large portion of the populace will have to make changes in how they work and support themselves.

“This anger we see among many people across our country feeling like they’re being left behind from the American dream, this report highlights that many of these same people are in the crosshairs of the impact of automation,” said Alastair Fitzpayne, executive director of the Future of Work Initiative at the Aspen Institute.

“Without policy intervention, the problems we see in our economy in terms of wage stagnation, labor participation, alarming levels of growth in low-wage jobs — those problems are likely to get worse, not better,” Fitzpayne told Recode. “Tech has a history that isn’t only negative if you look over the last 150 years. It can improve economic growth, it can create new jobs, it can boost people’s incomes, but you have to make sure the mechanisms are in place for that growth to be inclusive.”

Before we look at potential solutions, here are six charts that break down which groups are going to be affected most by the oncoming automation — and which have a better chance of surviving the robot apocalypse:

Occupation

The type of job you have largely affects your likelihood of being replaced by a machine. Jobs that require precision and repetition — food prep and manufacturing, for example — can be automated much more easily. Jobs that require creativity and critical thinking, like analysts and teachers, can’t as easily be recreated by machines. You can drill down further into which jobs fall under each job type here.

Education

People’s level of education greatly affects the types of work they are eligible for, so education and occupation are closely linked. Less education will more likely land a person in a more automatable job, while more education means more job options.

Age

Younger people are less likely to have attained higher degrees than older people; they’re also more likely to be in entry-level jobs that don’t require as much variation or decision-making as they might have later in life. Therefore, young people are more likely to be employed in occupations that are at risk of automation.

Race

The robot revolution will also increase racial inequality, as underrepresented minorities are more likely to hold jobs with tasks that could be automated — like food service, office administration, and agriculture.

Gender

Men, who have always been more likely to have better jobs and pay than women, also might be the first to have their jobs usurped. That’s because men tend to over-index in production, transportation, and construction jobs — all occupational groups that have tasks with above-average automation exposure. Women, meanwhile, are overrepresented in occupations related to human interaction, like health care and education — jobs that largely require human labor. Women are also now more likely to attain higher education degrees than men, meaning their jobs could be somewhat safer from being usurped by automation.

Location

Heartland states and rural areas — places that have large shares of employment in routine-intensive occupations like those found in the manufacturing, transportation, and extraction industries — contain a disproportionate share of occupations whose tasks are highly automatable. Small metro areas are also highly susceptible to job automation, though places with universities tend to be an exception. Cities — especially ones that are tech-focused and contain a highly educated populace, like New York; San Jose, California; and Chapel Hill, North Carolina — have the lowest automation potential of all.

See how your county could fare on the map below — the darker purple colors represent higher potential for automation:

Note that in none of the charts above are the percentages of tasks that could be automated very small — in most cases, the Brookings study estimates, at least 20 percent of any given demographic will see changes to their tasks due to automation. Of course, none of this means the end of work for any one group, but rather a transition in the way we work that won’t be felt equally.

“The fact that some of the groups struggling most now are among the groups that may face most challenges is a sobering thought,” said Mark Muro, a senior fellow at Brookings’s Metropolitan Policy Program.

In the worst-case scenario, automation will cause unemployment in the US to soar and exacerbate existing social divides. Depending on the estimate, anywhere from 3 million to 80 million people in the US could lose their jobs, so the implications could be dire.

“The Mad Max thing is possible, maybe not here but the impact on developing countries could be a lot worse as there was less stability to begin with,” said Martin Ford, author of Rise of the Robots and Architects of Intelligence. “Ultimately, it depends on the choices we make, what we do, how we can adapt.”

Fortunately, there are a number of potential solutions. The Brookings study and others lay out ways to mitigate job loss, and maybe even make the jobs of the future better and more attainable. The hardest part will be getting the government and private sector to agree on and pay for them. The Brookings policy recommendations include:

  • Create a universal adjustment benefit to laid-off workers. This involves offering career counseling, education, and training in new, relevant skills, and giving displaced workers financial support while they work on getting a new job. But as we know from the first manufacturing revolution, it’s difficult if not impossible to get government and corporations on board with aiding and reeducating displaced low-skilled workers. Indeed, many cities across the Rust Belt have yet to recover from the automation of car and steel plants in the last century. Government universal adjustment programs, which vary in cost based on their size and scope, provide a template but have had their own failings. Some suggest a carbon taxcould be a way to create billions of dollars in revenue for universal benefits or even universal basic income. Additionally, taxing income as opposed to labor — which could become scarcer with automation — provides other ways to fund universal benefits.
  • Maintain a full-employment economy. A focus on creating new jobs through subsidized employment programs will help create jobs for all who want them. Being employed will cushion some of the blow associated with transitioning jobs. Progressive Democrats’ proposed Green New Deal, which would create jobs geared toward lessening the United States’ dependence on fossil fuels, could be one way of getting to full employment. Brookings also recommends a federal monetary policy that prioritizes full employment over fighting inflation — a feasible action, but one that would require a meaningful change to the fed’s longstanding priorities.
  • Introduce portable benefits programs. This would give workers access to traditional employment benefits like health care, regardless of if or where they’re employed. If people are taken care of in the meantime, some of the stress of transitioning to new jobs would be lessened. These benefits also allow the possibility of part-time jobs or gig work — something that has lately become more of a necessity for many Americans. Currently, half of Americans get their health care through their jobs, and doctors and politicians have historically fought against government-run systems. The concept of portable benefits has recently been popular among freelance unions as well as among contract workers employed in gig economy jobs like Uber.
  • Pay special attention to communities that are hardest-hit. As we know from the charts above, some parts of America will have it way worse than others. But there are already a number of programs in place that provide regional protection for at-risk communities that could be expanded upon to deal with disruption from automation. The Department of Defense already does this on a smaller scale, with programs to help communities adjust after base closures or other program cancellations. Automation aid efforts would provide a variety of support, including grants and project management, as well as funding to convert facilities into new uses. Additionally, “Opportunity Zones” in the tax code — popular among the tech set — give companies tax breaks for investing in low-income areas. These investments in turn create jobs and stimulate spending in areas where it’s most needed.
  • Increased investment in AI, automation, and related technology. This may seem counterintuitive, seeing as automation is causing many of these problems in the first place, but Brookings believes that embracing this new tech — not erecting barriers to the inevitable — will generate the economic productivity needed to increase both standards of living and jobs outside of those that will be automated. “We are not vilifying these technologies; we are calling attention to positive side effects,” Brookings’s Muro said. “These technologies will be integral in boosting American productivity, which is dragging.”

None of these solutions, of course, is a silver bullet, but in conjunction, they could help mitigate some of the pain Americans will face from increased automation — if we act soon. Additionally, many of these ideas currently seem rather progressive, so they could be difficult to implement in a Republican-led government.

“I’m a long-run optimist. I think we will work it out. We have to — we have no choice,” Ford told Recode, emphasizing that humanity also stands to gain huge benefits from using AI and robotics to solve our biggest problems, like climate change and disease.

“The short term, though, could be tough — I worry about our ability to react in that time frame,” Ford said, especial given the current political climate. “But there comes a point when the cost of not adapting exceeds the cost of change.”

Source: The robot revolution will be worse for men