Bailey: Harnessing the Best of Automation While Minimizing the Downside Risks

A good summary of some of the issues. Arguably, the USA is ahead of us given our (over) reliance on immigration, both permanent and temporary, to address labour shortages rather that developing and implementing technologies:

The pandemic and economic disruptions have accelerated the adoption of automation technologies that will introduce important benefits to businesses and consumers but may also create disruptions for many workers and communities. Policymakers and leaders can take steps now to help navigate these disruptive changes.

Automation covers a broad range of technologies and advances in artificial intelligence (AI) and robotics that are deployed in novel ways to increase productivity or expand business capabilities. The Federal Reserve’s most recent Beige Book included observations from several districts noting that companies facing labor shortages were turning to automation as a solution. A McKinsey survey of 800 business executives found that 85 percent were accelerating their digitization and automation as a result of COVID-19. Companies across North America also spent a record $2 billion for almost 40,000 robots in 2021.

These new technologies are increasingly being deployed in a wide range of economic sectors. For example, in agriculture, drones such as the Agras MG-1 can provide precision irrigation for over 6,000 square meters of farmland in just under 10 minutes. John Deere is piloting autonomously driving tractors that can plow fields and plant crops with minimum human interaction. The autonomous robot created by Carbon Robotics can kill 100,000 weeds per hour, leading to increased crop yields, and reduce the use of pesticides by using nothing but lasers. 

These and other innovations will bring numerous benefits to businesses and consumers alike, but the transition could be disruptive to workers and communities. Policymakers should consider several actions to help harness the best of automation while minimizing the downside risks.

Community Dynamism. Policymakers and community leaders have a broad array of community development tools in their toolboxes, including Opportunity ZonesNew Markets Tax Credits, and Coronavirus State and Local Fiscal Recovery Funds. But they should first take a step back and consider how to create the conditions for dynamism, which AEI’s Ryan Streeter notes is “a culture rooted in a taste for discovery and betterment [that] can shape—indeed, has shaped—our institutions and policies, from how we structure patents to how we tax capital investments.” It offers a conceptual way to think through, structure, and orient all the existing policies and projects aimed at strengthening communities.

Boost Research and Development. The US must continue investing and expanding research and development on emerging technologies, including AI, to power the next generation of smart technologies, robotics, and drones. Addressing the computer chip shortage is critical, including bolstering domestic manufacturing capabilities. Various proposals being considered in the Bipartisan Innovation Act will advance this important work.

Invest in Human Capital. Automation is eroding jobs further up the skills ladder, which is raising the skill level for every new job while creating entirely new lines of work. Boston Consulting Group and the Burning Glass Institute analyzed more than 15 million job postings to understand how skill requests changed from 2016 to 2021. They found an acceleration in the pace of change. Nearly three-quarters of jobs changed more from 2019 through 2021 (with a compound annual growth rate of 22 percent) than they did from 2016 through 2018 (19 percent). The main driver was found to be technology, which redefined jobs sometimes radically and sometimes more subtly. The US should strengthen its entire skills pipeline to ensure individuals have the skills these jobs require. Community college programs will need to align with these new trends and employer needs. Companies should also explore apprenticeships to provide work-based learning opportunities for individuals transitioning careers. Skilled immigration, through ideas such as Heartland Visas, can also bolster the human capital available to communities.

Broadband Build-Out. State and community leaders must begin preparing their broadband plans to make the most of the $65 billion in new broadband fundingavailable through the Infrastructure Investment and Jobs Act. Connectivity enables smart devices and AI systems to talk to and coordinate with one another. It allows scaling of critical services, including telehealth and online job training. Leaders must begin developing their plans and priorities now to ensure projects support broader economic and community needs, prevent overbuilding, and ensure the funds build out future-proof infrastructure to underserved communities.

Regulatory Sandboxes. Policymakers should create regulatory sandboxes that invite experimentation with new technologies and automated systems. These are a win-win because they give policymakers the chance to better understand the new technologies they are responsible for regulating, while providing entrepreneurs and investors with clearer regulatory pathways and guardrails toward which they can develop. North Carolina launched a FinTech Regulatory Sandbox that allows pilot projects to test emerging technologies and business models, including technologies that would otherwise be illegal under existing regulations. Arizona created a regulatory pathway for safely developing and testing autonomous and connected vehicle technologies. These flexible regulatory environments can accelerate innovation and lead to smarter polices and regulations that protect consumers.

AI and automation will introduce important benefits to communities, businesses, and society. Policymakers and community leaders have important roles in helping to accelerate the use of these technologies while minimizing the disruption they pose for different communities.

Source: Harnessing the Best of Automation While Minimizing the Downside Risks

How America’s talent wars are reshaping business

In Canada, by contrast, immigration is relied upon to meet labour force requirements. One of the consequences, unforeseen or not, was reduced pressure to improve productivity and innovation:

Dcl logistics, like so many American firms, had a problem last year. Its business, fulfilling orders of goods sold online, faced surging demand. But competition for warehouse workers was fierce, wages were rising and staff turnover was high. So dcl made two changes. It bought robots to pick items off shelves and place them in boxes. And it reduced its reliance on part-time workers by hiring more full-time staff. “What we save in having temp employees, we lose in productivity,” explains Dave Tu, dcl’s president. Full-time payroll has doubled in the past year, to 280.Listen to this story

As American companies enter another year of uncertainty, the workforce has become bosses’ principal concern. Chief executives cite worker shortages as the greatest threat to their businesses in 2022, according to a survey by the Conference Board, a research organisation. On January 28th the Labour Department reported that firms had spent 4% more on wages and benefits in the fourth quarter, year on year, a rise not seen in 20 years. Paycheques of everyone from McDonald’s burger-flippers to Citi group bankers are growing fatter. This goes some way to explaining why profit margins in the s&p 500 index of large companies, which have defied gravity in the pandemic, are starting to decline. On February 2nd Meta spooked investors by reporting a dip in profits, due in part to a rise in employee-related costs as it moves from Facebook and its sister social networks into the virtual-reality metaverse.

At the same time, firms of all sizes and sectors are testing new ways to recruit, train and deploy staff. Some of these strategies will be temporary. Others may reshape American business.

The current jobs market looks extra ordinary by historical standards. December saw 10.9m job openings, up by more than 60% from December 2019. Just six workers were available for every ten open jobs (see chart 1). Predictably, many seem comfortable abandoning old positions to seek better ones. This is evident among those who clean bedsheets and stock shelves, as well as those building spreadsheets and selling stocks. In November 4.5m workers quit their jobs, a record. Even if rising wages and an ebbing pandemic lure some of them back to work, the fight for staff may endure.

For decades American firms slurped from a deepening pool of labour, as more women entered the workforce and globalisation greatly expanded the ranks of potential hires. That expansion has now mostly run its course, says Andrew Schwedel of Bain, a consultancy. Simultaneously, other trends have conspired to make the labour pool shallower than it might have been. Men continue to slump out of the job market: the share of men aged 25 to 54 either working or looking for work was 88% at the end of last year, down from 97% in the 1950s. Immigration, which plunged during Donald Trump’s nativist presidency, has sunk further, to less than a quarter of the level in 2016. And covid-19 may have prompted more than 2.4m baby boomers into early retirement, according to the Federal Reserve Bank of St Louis.

These trends will not reverse quickly. Boomers won’t sprint back to work en masse. With Republicans hostile to outsiders and Democrats squabbling over visas for skilled ones, a surge in immigration looks unlikely. Some men have returned to the workforce since the depths of the covid recession in 2020, but the male participation rate has plateaued below pre-pandemic levels. A tight labour market may persist.

But base pay is rising, too. Bank of America says it will raise its minimum wage to $25 by 2025. In September Walmart, America’s largest private employer, set its minimum wage at $12 an hour, below many states’ requirement of $13-14 but well above the federal minimum wage of $7.25. Amazon has lifted average wages in its warehouses to $18. The average hourly wage for production and nonsupervisory employees in December was 5.8% above the level a year earlier; compared with a 4.7% jump for all private-sector workers. Firms face pressure to lift them higher still. High inflation ensured that only workers in leisure and hospitality saw a real increase in hourly pay last year (see chart 2).

Raising compensation may not, on its own, be sufficient for companies to overcome the labour squeeze, however. This is where the other strategies come in, starting with changes to recruitment. To deal with the fact that, for some types of job, there simply are not enough qualified candidates to fill vacancies, many businesses are loosening hiring criteria previously deemed a prerequisite.

The share of job postings that list “no experience required” more than doubled from January 2020 to September 2021, reckons Burning Glass, an analytics firm. Easing rigid preconditions may be sensible, even without a labour shortage. A four-year degree, argues Joseph Fuller of Harvard Business School, is an unreliable guarantor of a worker’s worth. The Business Roundtable and the us Chamber of Commerce, two business groups, have urged companies to ease requirements that job applicants have a four-year university degree, advising them to value workers’ skills instead.

Another way to deal with a shortage of qualified staff is for firms to impart the qualifications themselves. In September, the most recent month for which Burning Glass has data, the share of job postings that offer training was more than 30% higher than in January 2020. New providers of training are proliferating, from university-run “bootcamps” to short-term programmes by specialists such as General Assembly and big employers themselves. Employers in Buffalo have hired General Assembly to run data-training schemes for local workers who are broadly able but who lack specific tech skills. Google, a technology giant, says it will consider workers who earn its online certificate in data analytics, for example, to be equivalent to a worker with a four-year degree.

Besides revamping recruitment and training, companies are modifying how their workers work. Some positions are objectively bad, with low pay, unpredictable scheduling and little opportunity for growth. Zeynep Ton of the mit Sloan School of Management contends that making low-wage jobs more appealing improves retention and productivity, which supports profits in the long term. As interesting as Walmart’s pay increases, she argues, are the retail behemoth’s management changes. Last year it said that two-thirds of the more than 565,000 hourly workers in its stores would work full time, up from about half in 2016. They would have predictable schedules week to week and more structured mentorship. Other companies may take note. Many of the complaints raised by labour organisers at Starbucks and Amazon have as much to do with safety and stress on the job as they do wages or benefits.

Companies that cannot find enough workers are trying to do with fewer of them. Sometimes that means trimming services. Many hotel chains, including Hilton, have made daily housekeeping optional. “We’ve been very thoughtful and cautious about what positions we fill,” Darren Woods, boss of ExxonMobil, told the oil giant’s investors on February 1st.

Increasingly, this also involves investments in automation. Orders of robots last year surpassed the pre-pandemic high in both volume and value, according to the Association for Advancing Automation. ups, a shipping firm, is boosting productivity with more automated bagging and labelling; new electronic tags will eliminate millions of manual scans each day.

New business models are pushing things along. Consider McEntire Produce in Columbia, South Carolina. Each year more than 45,000 tonnes of sliced lettuce, tomatoes and onions move through its factory. Workers pack them in bags, place bags in boxes and stack boxes on pallets destined for fast-food restaurants. McEntire has raised wages, but staff turnover remains high. Even as worker costs have climbed, the upfront expense of automation has sunk. So the firm plans to install new robots to box and stack. It will lease these from a new company called Formic, which offers robots at an hourly rate that is less than half the cost of a McEntire worker doing the same job. By 2025 McEntire wants to automate 60% of its volume, with robots handling the back-breaking work and workers performing tasks that require more skill. One new position, introduced in the past year, looks permanent: a manager whose sole job is to listen to and support staff so they do not quit. 

Both workers and employers are adapting. For the most part, they are doing so outside the construct of collective bargaining. Despite a flurry of activity—Starbucks baristas in Buffalo and Amazon workers in Alabama will hold union votes in February—unions remain weak. Last year 10.3% of American workers were unionised, matching the record low of 2019. Within the private sector, the unionisation rate is just 6.1%. Strikes and pickets will be a headache for some bosses. But it is quits that could cause them sleepless nights.

Pay as they go

Companies’ most straightforward tactic to deal with worker shortages is to raise pay. If firms are to part with cash, they prefer the inducements to be one-off rather than recurring and sticky, as with higher wages. That explains a proliferation of fat bonuses. Before the Christmas rush Amazon began offering workers a $3,000 sign-on sweetener. Compensation for lawyers at America’s top 50 firms rose by 16.5% last year, in part thanks to bonuses, according to a survey by Citigroup and Hildebrandt, a consultancy. In January Bank of America said it would give staff $1bn in restricted stock, which vests over time.

Source: How America’s talent wars are reshaping business

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Misattributed blame? Attitudes toward globalization in the age of automation

Interesting study and findings:

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

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

Pandemic Wave of Automation May Be Bad News for Workers

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Robots Are Coming for Phil in Accounting

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A white-collar wake-up call

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Humans defer to machines – professor

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

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

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

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

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

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

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

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

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

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

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

Don’t make assumptions on raw data – immigration adviser

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

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

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

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

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

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

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

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

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

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

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

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