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

The Hidden Automation Agenda of the Davos Elite

Clarity and likely greater impact on the labour force needed and immigration levels:

They’ll never admit it in public, but many of your bosses want machines to replace you as soon as possible.

I know this because, for the past week, I’ve been mingling with corporate executives at the World Economic Forum’s annual meeting in Davos. And I’ve noticed that their answers to questions about automation depend very much on who is listening.

In public, many executives wring their hands over the negative consequences that artificial intelligence and automation could have for workers. They take part in panel discussions about building “human-centered A.I.” for the “Fourth Industrial Revolution” — Davos-speak for the corporate adoption of machine learning and other advanced technology — and talk about the need to provide a safety net for people who lose their jobs as a result of automation.

But in private settings, including meetings with the leaders of the many consulting and technology firms whose pop-up storefronts line the Davos Promenade, these executives tell a different story: They are racing to automate their own work forces to stay ahead of the competition, with little regard for the impact on workers.

All over the world, executives are spending billions of dollars to transform their businesses into lean, digitized, highly automated operations. They crave the fat profit margins automation can deliver, and they see A.I. as a golden ticket to savings, perhaps by letting them whittle departments with thousands of workers down to just a few dozen.

“People are looking to achieve very big numbers,” said Mohit Joshi, the president of Infosys, a technology and consulting firm that helps other businesses automate their operations. “Earlier they had incremental, 5 to 10 percent goals in reducing their work force. Now they’re saying, ‘Why can’t we do it with 1 percent of the people we have?’”

Few American executives will admit wanting to get rid of human workers, a taboo in today’s age of inequality. So they’ve come up with a long list of buzzwords and euphemisms to disguise their intent. Workers aren’t being replaced by machines, they’re being “released” from onerous, repetitive tasks. Companies aren’t laying off workers, they’re “undergoing digital transformation.”

A 2017 survey by Deloitte found that 53 percent of companies had already started to use machines to perform tasks previously done by humans. The figure is expected to climb to 72 percent by next year.

The corporate elite’s A.I. obsession has been lucrative for firms that specialize in “robotic process automation,” or R.P.A. Infosys, which is based in India, reported a 33 percent increase in year-over-year revenue in its digital division. IBM’s “cognitive solutions” unit, which uses A.I. to help businesses increase efficiency, has become the company’s second-largest division, posting $5.5 billion in revenue last quarter. The investment bank UBS projects that the artificial intelligence industry could be worth as much as $180 billion by next year.

Kai-Fu Lee, the author of “AI Superpowers” and a longtime technology executive, predicts that artificial intelligence will eliminate 40 percent of the world’s jobs within 15 years. In an interview, he said that chief executives were under enormous pressure from shareholders and boards to maximize short-term profits, and that the rapid shift toward automation was the inevitable result.

The Milwaukee offices of the Taiwanese electronics maker Foxconn, whose chairman has said he plans to replace 80 percent of the company’s workers with robots in five to 10 years.CreditLauren Justice for The New York Times

“They always say it’s more than the stock price,” he said. “But in the end, if you screw up, you get fired.”

Other experts have predicted that A.I. will create more new jobs than it destroys, and that job losses caused by automation will probably not be catastrophic. They point out that some automation helps workers by improving productivity and freeing them to focus on creative tasks over routine ones.

But at a time of political unrest and anti-elite movements on the progressive left and the nationalist right, it’s probably not surprising that all of this automation is happening quietly, out of public view. In Davos this week, several executives declined to say how much money they had saved by automating jobs previously done by humans. And none were willing to say publicly that replacing human workers is their ultimate goal.

“That’s the great dichotomy,” said Ben Pring, the director of the Center for the Future of Work at Cognizant, a technology services firm. “On one hand,” he said, profit-minded executives “absolutely want to automate as much as they can.”

“On the other hand,” he added, “they’re facing a backlash in civic society.”

For an unvarnished view of how some American leaders talk about automation in private, you have to listen to their counterparts in Asia, who often make no attempt to hide their aims. Terry Gou, the chairman of the Taiwanese electronics manufacturer Foxconn, has said the company plans to replace 80 percent of its workers with robots in the next five to 10 years. Richard Liu, the founder of the Chinese e-commerce company JD.com, said at a business conferencelast year that “I hope my company would be 100 percent automation someday.”

One common argument made by executives is that workers whose jobs are eliminated by automation can be “reskilled” to perform other jobs in an organization. They offer examples like Accenture, which claimed in 2017 to have replaced 17,000 back-office processing jobs without layoffs, by training employees to work elsewhere in the company. In a letter to shareholders last year, Jeff Bezos, Amazon’s chief executive, said that more than 16,000 Amazon warehouse workers had received training in high-demand fields like nursing and aircraft mechanics, with the company covering 95 percent of their expenses.

But these programs may be the exception that proves the rule. There are plenty of stories of successful reskilling — optimists often cite a program in Kentucky that trained a small group of former coal miners to become computer programmers — but there is little evidence that it works at scale. A report by the World Economic Forum this month estimated that of the 1.37 million workers who are projected to be fully displaced by automation in the next decade, only one in four can be profitably reskilled by private-sector programs. The rest, presumably, will need to fend for themselves or rely on government assistance.

In Davos, executives tend to speak about automation as a natural phenomenon over which they have no control, like hurricanes or heat waves. They claim that if they don’t automate jobs as quickly as possible, their competitors will.

“They will be disrupted if they don’t,” said Katy George, a senior partner at the consulting firm McKinsey & Company.

Automating work is a choice, of course, one made harder by the demands of shareholders, but it is still a choice. And even if some degree of unemployment caused by automation is inevitable, these executives can choose how the gains from automation and A.I. are distributed, and whether to give the excess profits they reap as a result to workers, or hoard it for themselves and their shareholders.

The choices made by the Davos elite — and the pressure applied on them to act in workers’ interests rather than their own — will determine whether A.I. is used as a tool for increasing productivity or for inflicting pain.

“The choice isn’t between automation and non-automation,” said Erik Brynjolfsson, the director of M.I.T.’s Initiative on the Digital Economy. “It’s between whether you use the technology in a way that creates shared prosperity, or more concentration of wealth.”

Why no one really knows how many jobs automation will replace

Even though I have argued that immigration planning needs to factor in the possible impact of AI and automation, this note of caution should also be part of that analysis:

Tech CEOs and politicians alike have issued grave warnings about the capability of automation, including AI, to replace large swaths of our current workforce. But the people who actually study this for a living — economists — have very different ideas about just how large the scale of that automation will be.

For example, researchers at Citibank and the University of Oxford estimated that 57 percent of jobs in OECD countries — an international group of 36 nations including the U.S. — were at high risk of automation within the next few decades. In another well-cited study, researchers at the OECD calculated only 14 percent of jobs to be at high risk of automation within the same timeline. That’s a big range when you consider this means a difference of hundreds of millions of potential lost jobs in the next few decades.

Of course, technology also has the capability to create new jobs — or just change the nature of the work people are doing — rather than eliminate jobs altogether. But sizing the scope of sheer job loss is an important metric, because for every job lost, a member of the workforce will have to find a new one, oftentimes in an entirely different profession.

Even within the scope of the U.S., the estimates for how many jobs could be lost in a single year vary widely. Earlier this year, MIT Technology Review analyzed and plotted dozens of across-the-board predictions from researchers at places like McKinsey Global Institute, Gartner and the International Federation of Robotics. Here, we’ve charted some of the data they compiled, with some of our own analysis from additional reports:

So why do these predictions cover so much range? Recode asked leading academics and economists in the field and found some of the challenges in sizing how automation and similar technology will change the workforce:

Just because a technology exists doesn’t mean it’s going to be used

Even as new groundbreaking tech becomes available, there’s no guarantee that it will be implemented right away. For example, while autonomous-vehicle technology could one day eliminate or change the jobs of the estimated five million workers in the U.S. who drive professionally, there’s a long road ahead to getting legal clearance to do that.

“The fact that a job can be automated doesn’t mean it will be,” Glenda Quintini, a senior economist at the OECD, told Recode. “There’s a question of implementing, the cost of labor versus technology, and social desirability.”

Jobs involve a mix of tasks

Take the job of a waiter. A robot may be able to take over some aspects of that job, like taking orders, serving the food or handling payments. But other parts, like dealing with an angry customer, maybe less so. Some studies, such as the OECD report, assess the likelihood of each task within an occupation, while the Oxford studies make an overall assessment of each job.

There’s a debate among academics about which methodology makes more sense. The authors of the OECD report say that the granularity in their approach is more accurate, while the Oxford report authors argue that for most occupations, the detailed tasks don’t matter: As long as technology like AI can do the critical portion of the work, it ultimately has a binary “yes” or “no” capability to be automated.

The data isn’t good enough because it only measures what we know

To model the future, researchers have to start with data from the present — which is not always perfect. Economists do their best to take inventory of all the jobs out there and what tasks they involve, but this list admittedly isn’t exhaustive.

“There’s no assurance in the end that that we’ve captured every aspect of those jobs, so inevitably we might be overlooking some things,” said Carl Benedikt Frey, an economist at the University of Oxford.

It helps to know just how these experts make the predictions to fully understand the room for human error. In the case of the Oxford study, researchers gathered a list of hundreds of occupations and asked a panel of machine learning experts to make their best judgment as to whether or not some of those jobs were likely to be computerized. The researchers weighed in on only 70 out of the about 702 total jobs that they were most confident they could assess.

For the rest of the occupations, the researchers used an algorithm that attributed a numerical value to how much each job included tasks that are technology bottlenecks — things like “the ability to come up with unusual or clever ideas” or “persuading others to change their minds or behavior.” But ultimately, even that algorithmic modeling isn’t perfect, because not everybody agrees on just how socially complex any given job is. So while quantitative models can help reduce bias, they don’t eliminate it completely, and that can trickle down into differences in the final results.

For all these reasons, some academics prefer not to forecast an exact number of jobs lost in a specific timeframe, but instead focus on the relative percentage of jobs in an economy at risk.

“All of these studies that have tried to put a number on how many jobs are going to be lost in a decade or two decades or five years — they’re trying to do something that is just impossible,” Frey said.

Economist John Maynard Keynes famously said that by 2030, due to rapid advancements in technology, we’d see widespread “technological unemployment” and be working an average of only 15 hours a week. It was a positive vision for a world where mankind would finally have “freedom from pressing economic cares” and live a life of leisure. Those estimates seem widely overblown now. While Keynes was right that technology has helped increase productivity in entirely new industries, the average workweek in the U.S. hasn’t declined since the 1970s.

Thanks in large part to persistent wage stagnation and rising income inequality in the last few decades, most people still have to work just as many hours as they did before in order to make ends meet.

Keynes’s comments remind us that there’s a bad track record of punditry in this field, and that even the greats can be wrong when it comes to predicting just how much, or how fast, technology will impact the workforce.

Source: Why no one really knows how many jobs automation will replace

Machines Will Handle More Than Half of Workplace Tasks by 2025, WEF Report Says

Question is: what kind of jobs and will it truly be a “positive impact:”

Organizers of the Davos forum say in a new report that machines are increasingly moving in on jobs done by people, projecting that more than half of all workplace tasks will be carried out by machines by 2025.

The World Economic Forum also predicts the loss of some 75 million jobs worldwide by 2022, but also says 133 million new jobs will be created.

The WEF said Monday: “Despite bringing widespread disruption, the advent of machine, robots and algorithm could actually have a positive impact on human employment.”

The “Future of Jobs 2018” report, the second of its kind, is based on a survey of executives representing 15 million employees in 20 economies.

The WEF said challenges for employers include reskilling workers, enabling remote employment and building safety nets for workers.

Source: Machines Will Handle More Than Half of Workplace Tasks by 2025, a Report Says