The Work of the Future: Building Better Jobs in an Age of Intelligent Machines

Curated on Posted on by Stefaan Verhulst

Book by By David Autor, David A. Mindell and Elisabeth B. Reynolds: “The United States has too many low-quality, low-wage jobs. Every country has its share, but those in the United States are especially poorly paid and often without benefits. Meanwhile, overall productivity increases steadily and new technology has transformed large parts of the economy, enhancing the skills and paychecks of higher-paid knowledge workers. What’s wrong with this picture? Why have so many workers benefited so little from decades of growth? The Work of the Future shows that technology is neither the problem nor the solution. We can build better jobs if we create institutions that leverage technological innovation and also support workers though long cycles of technological transformation.

Building on findings from the multiyear MIT Task Force on the Work of the Future, the book argues that we must foster institutional innovations that complement technological change. Skills programs that emphasize work-based and hybrid learning (in person and online), for example, empower workers to become and remain productive in a continuously evolving workplace. Industries fueled by new technology that augments workers can supply good jobs, and federal investment in R&D can help make these industries worker-friendly. We must act to ensure that the labor market of the future offers benefits, opportunity, and a measure of economic security to all….(More)”.

The Digital Continent: Placing Africa in Planetary Networks of Work

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Open Access Book by Mohammad Amir Anwar and Mark Graham: “As recently as the early 2010s, there were more internet users in countries like France or Germany than in all of Africa put together. But much changed in that decade, and 2018 marked the first year in human history in which a majority of the world’s population is now connected to the internet. This mass connectivity means that we have an internet that no longer connects only the world’s wealthy. Workers from Lagos to Johannesburg to Nairobi, and everywhere in between, can now apply for and carry out jobs coming from clients who themselves can be located anywhere in the world. Digital outsourcing firms can now also set up operations in the most unlikely of places in order to tap into hitherto disconnected labour forces. With CEOs in the Global North proclaiming that location is a concern of the past, and governments and civil society in Africa promising to create millions of jobs on the continent, The Digital Continent investigates what this new world of digital work means to the lives of African workers. Anwar and Graham draw on a five-year-long field study in South Africa, Kenya, Nigeria, Ghana, and Uganda, and over 200 interviews conducted with participants including gig workers, call and contact centre workers, small self-employed freelancers, business owners, government officials, labour union officials, and industry experts. Focusing on both platform-based remote work and call and contact centre work, the book examines the job quality implications of digital work for the lives and livelihoods of African workers…(More)”.

How digital transformation is driving economic change

Curated on Posted on by Stefaan Verhulst

Blog (and book) by Zia Qureshi: “We are living in a time of exciting technological innovations. Digital technologies are driving transformative change. Economic paradigms are shifting. The new technologies are reshaping product and factor markets and profoundly altering business and work. The latest advances in artificial intelligence and related innovations are expanding the frontiers of the digital revolution. Digital transformation is accelerating in the wake of the COVID-19 pandemic. The future is arriving faster than expected.

A recently published book, “Shifting Paradigms: Growth, Finance, Jobs, and Inequality in the Digital Economy,” examines the implications of the unfolding digital metamorphosis for economies and public policy agendas….

Firms at the technological frontier have broken away from the rest, acquiring dominance in increasingly concentrated markets and capturing the lion’s share of the returns from the new technologies. While productivity growth in these firms has been strong, it has stagnated or slowed in other firms, depressing aggregate productivity growth. Increasing automation of low- to middle-skill tasks has shifted labor demand toward higher-level skills, hurting wages and jobs at the lower end of the skill spectrum. With the new technologies favoring capital, winner-take-all business outcomes, and higher-level skills, the distribution of both capital and labor income has tended to become more unequal, and income has been shifting from labor to capital.

One important reason for these outcomes is that policies and institutions have been slow to adjust to the unfolding transformations. To realize the promise of today’s smart machines, policies need to be smarter too. They must be more responsive to change to fully capture potential gains in productivity and economic growth and address rising inequality as technological disruptions create winners and losers.

As technology reshapes markets and alters growth and distributional dynamics, policies must ensure that markets remain inclusive and support wide access to the new opportunities for firms and workers. The digital economy must be broadened to disseminate new technologies and opportunities to smaller firms and wider segments of the labor force…(More)”.

Economists Pin More Blame on Tech for Rising Inequality

Curated on Posted on by Stefaan Verhulst

Steve Lohr at the New York Times: “Daron Acemoglu, an influential economist at the Massachusetts Institute of Technology, has been making the case against what he describes as “excessive automation.”

The economywide payoff of investing in machines and software has been stubbornly elusive. But he says the rising inequality resulting from those investments, and from the public policy that encourages them, is crystal clear.

Half or more of the increasing gap in wages among American workers over the last 40 years is attributable to the automation of tasks formerly done by human workers, especially men without college degrees, according to some of his recent research…

Mr. Acemoglu, a wide-ranging scholar whose research makes him one of most cited economists in academic journals, is hardly the only prominent economist arguing that computerized machines and software, with a hand from policymakers, have contributed significantly to the yawning gaps in incomes in the United States. Their numbers are growing, and their voices add to the chorus of criticism surrounding the Silicon Valley giants and the unchecked advance of technology.

Paul Romer, who won a Nobel in economic science for his work on technological innovation and economic growth, has expressed alarm at the runaway market power and influence of the big tech companies. “Economists taught: ‘It’s the market. There’s nothing we can do,’” he said in an interview last year. “That’s really just so wrong.”

Anton Korinek, an economist at the University of Virginia, and Joseph Stiglitz, a Nobel economist at Columbia University, have written a paper, “Steering Technological Progress,” which recommends steps from nudges for entrepreneurs to tax changes to pursue “labor-friendly innovations.”

Erik Brynjolfsson, an economist at Stanford, is a technology optimist in general. But in an essay to be published this spring in Daedalus, the journal of the American Academy of Arts and Sciences, he warns of “the Turing trap.” …(More)”

Group Backed by Top Companies Moves to Combat A.I. Bias in Hiring

Curated on Posted on by Stefaan Verhulst

Steve Lohr at The New York Times: “Artificial intelligence software is increasingly used by human resources departments to screen résumés, conduct video interviews and assess a job seeker’s mental agility.

Now, some of the largest corporations in America are joining an effort to prevent that technology from delivering biased results that could perpetuate or even worsen past discrimination.

The Data & Trust Alliance, announced on Wednesday, has signed up major employers across a variety of industries, including CVS Health, Deloitte, General Motors, Humana, IBM, Mastercard, Meta (Facebook’s parent company), Nike and Walmart.

The corporate group is not a lobbying organization or a think tank. Instead, it has developed an evaluation and scoring system for artificial intelligence software.

The Data & Trust Alliance, tapping corporate and outside experts, has devised a 55-question evaluation, which covers 13 topics, and a scoring system. The goal is to detect and combat algorithmic bias.“This is not just adopting principles, but actually implementing something concrete,” said Kenneth Chenault, co-chairman of the group and a former chief executive of American Express, which has agreed to adopt the anti-bias tool kit…(More)”.

Do we know what jobs are in high demand?

Curated on Posted on by Stefaan Verhulst

Emma Rindlisbacher at Work Shift: “…Measuring which fields are in demand is harder than it sounds. Many of the available data sources, experts say, have significant flaws. And that causes problems for education providers who are trying to understand market demand and map their programs to it.

“If you are in higher education and trying to understand where the labor market is going, use BLS data as a general guide but do not rely too heavily on it when it comes to building programs and making investments,” said Jason Tyszko, the Vice President of the Center for Education and Workforce at the US Chamber of Commerce Foundation.

What’s In-Demand?

Why it matters: Colleges are turning to labor market data as they face increasing pressure from lawmakers and the public to demonstrate value and financial ROI. A number of states also have launched specialized grant and “free college” programs for residents pursuing education in high-demand fields. And many require state agencies to determine which fields are in high demand as part of workforce planning processes.

Virginia is one of those states. To comply with state law, the Board of Workforce Development has to regularly update a list of high demand occupations. Deciding how to do so can be challenging.

According to a presentation given at a September 2021 meeting, the board chose to determine which occupations are in high demand by using BLS data. The reason: the BLS data is publicly available.

“Although in some instances, proprietary data sources have different or additional nuances, in service of guiding principle #1 (transparency, replicability), our team has relied exclusively on publicly available data for this exercise,” the presentation said. (A representative from the board declined to comment, citing the still ongoing nature of constructing the high demand occupations list.)

The limits of the gold standard

For institutions looking to study job market trends, there are typically two main data sources available. The first, from BLS, are official government statistics primarily designed to track economic indicators such as the unemployment rate. The second, from proprietary companies such as Emsi Burning Glass, typically relies on postings to job board websites like LinkedIn. 

The details: The two sources have different strengths and weaknesses. The Emsi Burning Glass data can be considered “real time” data, because it identifies new job postings as they are released online. The BLS data, on the other hand, is updated less frequently but is comprehensive.

The BLS data is designed to compare economic trends across decades, and to map to state systems so that statistics like unemployment rates can be compared across states. For those reasons, the agency is reluctant to change the definitions underlying the data. That consistency, however, can make it difficult for education providers to use the data to determine which fields are in high demand.

BLS data is broken down according to the Standard Occupation Classification system, or SOC, a taxonomy used to classify different occupations. That taxonomy is designed to be public facing—the BLS website, for example, features a guide for job seekers that purports to tell them which occupation codes have the highest wages or the greatest potential for growth.

But the taxonomy was last updated in 2010, according to a BLS spokesperson…(More)”.

New York City passed a bill requiring ‘bias audits’ of AI hiring tech

Curated on Posted on by Stefaan Verhulst

Kate Kaye at Protocol: “Let the AI auditing vendor brigade begin. A year since it was introduced, New York City Council passed a bill earlier this week requiring companies that sell AI technologies for hiring to obtain audits assessing the potential of those products to discriminate against job candidates. The bill requiring “bias audits” passed with overwhelming support in a 38-4 vote.

The bill is intended to weed out the use of tools that enable already unlawful employment discrimination in New York City. If signed into law, it will require providers of automated employment decision tools to have those systems evaluated each year by an audit service and provide the results to companies using those systems.

AI for recruitment can include software that uses machine learning to sift through resumes and help make hiring decisions, systems that attempt to decipher the sentiments of a job candidate, or even tech involving games to pick up on subtle clues about someone’s hiring worthiness. The NYC bill attempts to encompass the full gamut of AI by covering everything from old-school decision trees to more complex systems operating through neural networks.

The legislation calls on companies using automated decision tools for recruitment not only to tell job candidates when they’re being used, but to tell them what information the technology used to evaluate their suitability for a job.

The bill, however, fails to go into detail on what constitutes a bias audit other than to define one as “an impartial evaluation” that involves testing. And it already has critics who say it was rushed into passage and doesn’t address discrimination related to disability or age…(More)”.

We Need a New Economic Category

Curated on Posted on by Stefaan Verhulst

Article by Anne-Marie Slaughter and Hilary Cottam: “Recognizing the true value and potential of care, socially as well as economically, depends on a different understanding of what care actually is: not a service but a relationship that depends on human connection. It is the essence of what Jamie Merisotis, the president of the nonprofit Lumina Foundation, calls “human work”: the “work only people can do.” This makes it all the more essential in an age when workers face the threat of being replaced by machines.

When we use the word in an economic sense, care is a bundle of services: feeding, dressing, bathing, toileting, and assisting. Robots could perform all of those functions; in countries such as Japan, sometimes they already do. But that work is best described as caretaking, comparable to what the caretaker of a property provides by watering a garden or fixing a gate.

What transforms those services into caregiving, the support we want for ourselves and for those we love, is the existence of a relationship between the person providing care and the person being cared for. Not just any relationship, but one that is affectionate, or at least considerate and respectful. Most human beings cannot thrive without connection to others, a point underlined by the depression and declining mental capacities of many seniors who have been isolated during the pandemic….

One of us, Hilary, has worked in Britain to expand caregiving networks. In 2007 she co-designed a program called Circle, which is part social club, part concierge service. Members pay a small monthly fee, and in return get access to fun activities and practical support from members and helpers in the community. More than 10,000 people have participated, and evaluations show that members feel less lonely and more capable. The program has also reduced the money spent on formal services; Circle members are less likely, for example, to be readmitted to the hospital.The mutual-aid societies that mushroomed into existence across the United States during the pandemic reflect the same philosophy. The core of a mutual-aid network is the principle of “solidarity not charity”: a group of community members coming together on an equal basis for the common good. These societies draw on a long tradition of “collective care” developed by African American, Indigenous, and immigrant groups as far back as the 18th century….Care jobs help humans flourish, and, properly understood and compensated, they can power a growing sector of the economy, strengthen our society, and increase our well-being. Goods are things that people buy and own; services are functions that people pay for. Relationships require two people and a connection between them. We don’t really have an economic category for that, but we should….(More)”.

A crowdsourced spreadsheet is the latest tool in Chinese tech worker organizing

Curated on Posted on by Stefaan Verhulst

Article by JS: “This week, thousands of Chinese tech workers are sharing information about their working schedules in an online spreadsheet. Their goal is to inform each other and new employees about overtime practices at different companies. 

This initiative for work-schedule transparency, titled Working Time, has gone viral. As of Friday—just three days after the project launched—the spreadsheet has already had millions of views and over 6000 entries. The creators also set up group chats on the Tencent-owned messaging platform, QQ, to invite discussion about the project—over 10000 people have joined as participants.

This initiative comes after the explosive 996.ICU campaign which took place in 2019 where hundreds of thousands of tech workers in the country participated in an online effort to demand the end of the 72-hour work week—9am to 9pm, 6 days a week.

This year, multiple tech companies—with encouragement from the government—have ended overtime work practices that forced employees to work on Saturdays (or in some cases, alternating Saturdays). This has effectively ended 996, which was illegal to begin with. While an improvement, the data collected from this online spreadsheet shows that most tech workers still work long hours, either “1095” or “11105” (10am to 9pm or 11am to 10pm, 5 days a week). The spreadsheet also shows a non-negligible number of workers still working 6 days week.

Like the 996.ICU campaign, the creators of this spreadsheet are using GitHub to circulate and share info about the project. The first commit was made on Tuesday, October 12th. Only a few days later, the repo has been starred over 9500 times….(More)”.

The Society of Algorithms

Curated on Posted on by Stefaan Verhulst

Paper by Jenna Burrell and Marion Fourcade: “The pairing of massive data sets with processes—or algorithms—written in computer code to sort through, organize, extract, or mine them has made inroads in almost every major social institution. This article proposes a reading of the scholarly literature concerned with the social implications of this transformation. First, we discuss the rise of a new occupational class, which we call the coding elite. This group has consolidated power through their technical control over the digital means of production and by extracting labor from a newly marginalized or unpaid workforce, the cybertariat. Second, we show that the implementation of techniques of mathematical optimization across domains as varied as education, medicine, credit and finance, and criminal justice has intensified the dominance of actuarial logics of decision-making, potentially transforming pathways to social reproduction and mobility but also generating a pushback by those so governed. Third, we explore how the same pervasive algorithmic intermediation in digital communication is transforming the way people interact, associate, and think. We conclude by cautioning against the wildest promises of artificial intelligence but acknowledging the increasingly tight coupling between algorithmic processes, social structures, and subjectivities….(More)”.