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

How we stopped worrying and learned to love robots

Still looking for someone to translate the expected AI impact in terms of what it means in terms of immigration levels and skills:

As Bob Magee, chairman of the Woodbridge Group, walked us through his foam-manufacturing facility just north of Toronto, a familiar story emerged. Automation for this company isn’t a simple calculation of substituting one machine for one worker. Rather, it is one of many incremental steps in a process of continuous improvement that requires engaged employees at each and every step.

At the Woodbridge Group, automation is beneficial for the firm and workers alike. It contributes to improved competitiveness — a necessary precondition for jobs — while making existing jobs easier, more efficient and, from our observations, more enjoyable. Where workers were once required to lift and place heavy sheets of foam, these tasks are now done by machines. Workers are free to do what they do best: oversee processes, ensure quality and work as a team to make the plant more efficient.

Despite many stories like these, concerns over automation decimating the workforce and leaving millions unemployed persist. Is automation driving us toward a jobless future or a more productive and prosperous economy for firms and workers alike? To better understand what’s happening and what’s coming in Ontario, Ontario’s Ministry of Economic Development and Growth and Ministry of Advanced Education and Skills Development commissioned the Brookfield Institute to take a closer look. Our in-depth analysis included systematic reviews of existing literature and data, interviews with over 50 people representing labour, business and developers of technology, and a two-phase citizen engagement process in communities across the province involving roughly 300 individuals. Our work and findings were overseen and reviewed by an expert advisory panel of 14 people with technology, academic and industry expertise.

The extent to which communities and workers are impacted by automation depends on the behaviour of firms — that is, whether they invest in automation technologies. This decision is influenced by a myriad of internal and external factors, including domestic and international competition, changing consumer preferences and the need to maintain output as workers age and retire. The ultimate goal of automation is always to improve productivity, product quality and overall competitiveness.

Given Ontario firm’s track record on technology adoption, large-scale disruption is likely not around the corner. Just as many factors influence tech adoption, others impede it. These include cost barriers and risk aversion, the difficulties associated with integrating new technology in existing legacy systems and — surprisingly — shortages of workers with the skills to properly implement and maintain technology.

For many firms in Ontario these barriers significantly inhibit technological adoption. The gap in information and communications technology (ICT) investment between Ontario and the US is substantial and has grown in recent years. In 2015, Ontario firms’ annual ICT investment was 2.39 percent as a share of GDP, versus 3.15 percent in the US and 2.16 for Canada as a whole. This disparity puts a damper on predictions of an imminent automation-driven jobless future.

If Ontario firms continue to lag when it comes to tech adoption, the associated decline in competitiveness could spell disaster for them and their workers.

In the Canadian manufacturing sector (to which Ontario manufacturers contributed roughly 47 percent of output in 2016), firms’ ICT investment per worker was 57 percent of that of their US counterparts, as of 2013. Despite this lower rate of investment in technology, Ontario experienced sharper declines in employment (5.5 percent from 2001 to 2011) than both the US (4.2 percent) and Germany (4 percent) — jurisdictions with higher rates of technology adoption. This suggests that while automation has enabled many manufacturers to produce more goods with fewer people, low rates of technology adoption may also be a concern for workers.

Without skilled workers, automation simply would not be possible. They are needed at each and every step, to identify inefficiencies and to integrate and oversee technology.

When new technologies are adopted, the impact on workers is a function of how those specific technologies affect business activities, what new skills are needed as a result and whether these new skills are present in the firm’s existing workforce and in the broader labour market.

Automation can help firms retain existing jobs, albeit with different skill requirements. In some instances, employees can be redeployed, often to more interesting, productive and safe work. Automation can also help existing firms expand and new businesses form. Historically, automation has created more jobs than it eliminates, in the long run.

In Ontario’s finance and insurance sector, for example, automation has contributed to improved efficiency, yet employment continues to rise. Between 2002 and 2016, the number of workers required to generate $1 million in output declined from 5.9 to 5.2, but employment expanded by 35 percent, or 85,350 workers. But automation has also contributed to significant shifts in skill requirements, increasing demand for both soft and technical skills, including those related to client experience, sales, and project and risk management, as well as software development and data analysis. This shift is perhaps best exemplified by the impact of the ATM on bank tellers, whose numbers actually increased after ATMs were introduced.

Automation can eliminate certain kinds of job tasks and sometimes whole occupations. When new jobs are created, they often require different skill sets and frequently emerge in industries and regions different than those where jobs might have been lost. If workers are unable to move, to acquire new skills to adapt or to change jobs, they may experience a prolonged adjustment period of underemployment or unemployment. This in turn can depress local labour markets and exacerbate the inequitable distribution of wealth among individuals and across regions.

For workers and firms to be successful, Ontario must overcome barriers and embrace automation with an intensity comparable to that of our international peers. This process will require a skilled workforce able to support technological adoption. For many workers, the benefits are clear: jobs will be retained and may even get better. But an increased pace of automation could leave others behind. We need to ensure that workers have the skills and opportunities to adapt to and even drive automation. This will require more than incremental changes, and our public and private sectors will need to rethink and better coordinate existing programs geared toward promoting technological adoption and delivering skills training.

Source: How we stopped worrying and learned to love robots

Get ready: A massive automation shift is coming for your job

Still waiting for some of the entities proposing increased immigration (e.g., Barton Commission, Century Initiative) to factor this into their thinking. The Conference Board has at least acknowledged the issue:

The robots are coming to take our jobs and Canada must do a lot more to deal with it.

That’s not the prediction of a doomsday prophet, but of the world’s leading business consultant, the managing director of global firm McKinsey & Co. and chair of the Canadian government’s Advisory Council on Economic Growth, Dominic Barton.

Okay, admittedly Mr. Barton didn’t exactly say the robots are taking over the planet. But he is warning that automation – robots, driverless cars, artificial intelligence, technological transformation – will disrupt millions of Canadian jobs, not far in the future, but in the next dozen years.

Put another way: If you are 30 or 35 now, there’s a good chance that not just your job, but the kind of job you do, will be eliminated – at the most inopportune time of life, when you are 40 to 55, perhaps with a mortgage and kids.

The council that Mr. Barton heads is calling for a national “re-skilling” effort that would cost $15-billion a year – per year – to help Canadians cope. He doesn’t think all that money can come from government, but he thinks it’s going to have to come from somewhere.

“The scale of the change is so significant. What are we doing to really get at that?” Mr. Barton said over the phone from Melbourne, Australia. “We’re talking a really big issue.”

This issue is a massive sleeper test for the government. It’s a test for all governments, really, but in this country it’s a test of ambition for Justin Trudeau’s Liberal government. It could well be the biggest societal issue of our time. Finance Minister Bill Morneau’s next budget will be delivered in less than two weeks. Will it even begin to reflect the scope of the issue?

To be fair, Mr. Morneau’s last budget talked a lot about job training, and it put some modest sums into it. Mr. Morneau, who ran a human-resources firm, was talking about these issues before he was elected as an MP. But there isn’t yet a government response from Ottawa that hints at the scale of Mr. Barton’s warning.

He is talking about vast change, soon. There are driverless cars now, he noted. That makes it easy to see the prospect of truck drivers thrown out of work en masse. (The courier firm FedEx has hinted its driverless vehicle plans aren’t so far away; the company has 400,000 employees.)

It’s not just truck drivers or factory workers who could see their jobs washed away by technological change. It includes knowledge workers, such as well-paid wealth managers who could find their current jobs automated. The Advisory Council estimated 10 to 12 per cent of Canadian workers could see their jobs disrupted by technology by 2030. “That’s two million people,” he noted. Mr. Barton thinks the estimate is conservative.

That’s different from when a company goes bankrupt or a plant closes, and laid-off workers go look for the same job at another company. Technological change will wipe out occupations. People will need to do new kinds of work, and they will need new skills. Technology might also create millions of jobs, but if Canadians don’t have the skills, a lot of those jobs might go to the United States or China or Sweden.

If you’ve watched the way voters in the United States and elsewhere have responded to disruptions of well-paying manufacturing jobs and good job opportunities, how it has fuelled divisive politics, an anti-trade backlash, and anti-immigrant nativism, just imagine how society could be roiled by two million middle-aged Canadians looking for work without much idea how they’re going to start over.

The Advisory Council argued that it has to be met with a major revamp of job training and lifelong education and a $15-billion injection of resources.

It’s an enormous sum, about three-quarters of the cost of the military. It’s too much for federal and provincial governments to pay alone, he argues, but business will have to be given incentives to do more education and training. Individuals, even those who feel squeezed saving for retirement, will have to save for lifelong learning, perhaps with tax-sheltered learning accounts. They won’t have a choice, he believes, “because it’s coming.”

The advisory council was appointed by the Liberals, and Mr. Barton has the ear of Mr. Trudeau and his inner circle. The Liberal government has adopted a lot of the council’s recommendations, to varying degrees, in its strategy to foster economic growth. But Mr. Barton noted the one with the biggest estimate impact is that massive re-skilling initiative. So far, governments are working on the same scale to face up to the impact of automation, but they will have to face it sooner or later. It’s coming.

via Get ready: A massive automation shift is coming for your job – The Globe and Mail

Will Robots Take Our Children’s Jobs? – The New York Times

Good read by Alex Williams on the occupations most likely to be threatened and the coming disruption:

But artificial intelligence is different, said Martin Ford, the author of “Rise of the Robots: Technology and the Threat of a Jobless Future.” Machine learning does not just give us new machines to replace old machines, pushing human workers from one industry to another. Rather, it gives us new machines to replace us, machines that can follow us to virtually any new industry we flee to.

Since Mr. Ford’s book sent me down this rabbit hole in the first place, I reached out to him to see if he was concerned about all this for his own children: Tristan, 22, Colin, 17, and Elaine, 10.

He said the most vulnerable jobs in the robot economy are those involving predictable, repetitive tasks, however much training they require. “A lot of knowledge-based jobs are really routine — sitting in front of a computer and cranking out the same application over and over, whether it is a report or some kind of quantitative analysis,” he said.

Professions that rely on creative thinking enjoy some protection (Mr. Ford’s older son is a graduate student studying biomedical engineering). So do jobs emphasizing empathy and interpersonal communication (his younger son wants to be a psychologist).

Even so, the ability to think creatively may not provide ultimate salvation. Mr. Ford said he was alarmed in May when Google’s AlphaGo software defeated a 19-year-old Chinese master at Go, considered the world’s most complicated board game.

“If you talk to the best Go players, even they can’t explain what they’re doing,” Mr. Ford said. “They’ll describe it as a ‘feeling.’ It’s moving into the realm of intuition. And yet a computer was able to prove that it can beat anyone in the world.”

Looking for a silver lining, I spent an afternoon Googling TED Talks with catchy titles like “Are Droids Taking Our Jobs?”

In one, Albert Wenger, an influential tech investor, promoted the Basic Income Guarantee concept. Also known as Universal Basic Income, this sunny concept holds that a robot-driven economy may someday produce an unlimited bounty of cool stuff while simultaneously releasing us from the drudgery of old-fashioned labor, leaving our government-funded children to enjoy bountiful lives of leisure as interpretive dancers or practitioners of bee-sting therapy, as touted by Gwyneth Paltrow.

The idea is all the rage among Silicon Valley elites, who not only understand technology’s power, but who also love to believe that it will be used for good. In their vision of a post-A.I. world without traditional jobs, everyone will receive a minimum weekly or monthly stipend (welfare for all, basically).

Another talk by David Autor, an economist, argued that reports of the death of work are greatly exaggerated. Almost 50 years after the introduction of the A.T.M., for instance, more humans actually work as bank tellers than ever. The computers simply freed the humans from mind-numbing work like counting out 20-dollar bills to focus on more cognitively demanding tasks like “forging relationships with customers, solving problems and introducing them to new products like credit cards, loans and investments,” he said.

Computers, after all, are really good at some things and, for the moment, terrible at others. Even Anton intuits this. The other day I asked him if he thought robots were smarter or dumber than humans. “Sdumber,” he said after a long pause. Confused, I pushed him. “Smarter and dumber,” he explained with a cheeky smile.

He was joking. But he also happened to be right, according to Andrew McAfee, a management theorist at the Massachusetts Institute of Technology whom I interviewed a short while later.

Discussing another of Anton’s career aspirations — songwriter — Dr. McAfee said that computers were already smart enough to come up with a better melody than a lot of humans. “The things our ears find pleasant, we know the rules for that stuff,” he said. “However, I’m going to be really surprised when there is a digital lyricist out there, somebody who can put words to that music that will actually resonate with people and make them think something about the human condition.”

Not everyone, of course, is cut out to be a cyborg-Springsteen. I asked Dr. McAfee what other jobs may exist a decade from now.

“I think health coaches are going to be a big industry of the future,” he said. “Restaurants that have a very good hospitality staff are not about to go away, even though we have more options to order via tablet.

“People who are interested in working with their hands, they’re going to be fine,” he said. “The robot plumber is a long, long way away.”

via Will Robots Take Our Children’s Jobs? – The New York Times