Governing AI to Advance Shared Prosperity

Chapter by Ekaterina Klinova: “This chapter describes a governance approach to promoting AI research and development that creates jobs and advances shared prosperity. Concerns over the labor-saving focus of AI advancement are shared by a growing number of economists, technologists, and policymakers around the world. They warn about the risk of AI entrenching poverty and inequality globally. Yet, translating those concerns into proactive governance interventions that would steer AI away from generating excessive levels of automation remains difficult and largely unattempted. Key causes of this difficulty arise from two types of sources: (1) insufficiently deep understanding of the full composition of factors giving AI R&D its present emphasis on labor-saving applications; and (2) lack of tools and processes that would enable AI practitioners and policymakers to anticipate and assess the impact of AI technologies on employment, wages and job quality. This chapter argues that addressing (2) will require creating worker-participatory means of differentiating between genuinely worker-benefiting AI and worker-displacing or worker-exploiting AI. To contribute to tackling (1), this chapter reviews AI practitioners’ motivations and constraints, such as relevant laws, market incentives, as well as less tangible but still highly influential constraining and motivating factors, including explicit and implicit norms in the AI field, visions of future societal order popular among the field’s members and ways that AI practitioners define goals worth pursuing and measure success. I highlight how each of these factors contributes meaningfully to giving AI advancement its excessive labor-saving emphasis and describe opportunities for governance interventions that could correct that over emphasis….(More)”.

The effects of AI on the working lives of women

Report by Clementine Collett, Gina Neff and Livia Gouvea: “Globally, studies show that women in the labor force are paid less, hold fewer senior positions and participate less in science, technology, engineering and mathematics (STEM) fields. A 2019 UNESCO report found that women represent only 29% of science R&D positions globally and are already 25% less likely than men to know how to leverage digital technology for basic uses.

As the use and development of Artificial Intelligence (AI) continues to mature, its time to ask: What will tomorrows labor market look like for women? Are we effectively harnessing the power of AI to narrow gender equality gaps, or are we letting these gaps perpetuate, or even worse, widen?

This collaboration between UNESCO, the Inter-American Development Bank (IDB) and the Organisation for Economic Co-operation and Development (OECD) examines the effects of the use of AI on the working lives of women. By closely following the major stages of the workforce lifecycle from job requirements, to hiring to career progression and upskilling within the workplace – this joint report is a thorough introduction to issues related gender and AI and hopes to foster important conversations about womens equality in the future of work…(More)”

Automation exacts a toll in inequality

Rana Foroohar at The Financial Times: “When humans compete with machines, wages go down and jobs go away. But, ultimately, new categories of better work are created. The mechanisation of agriculture in the first half of the 20th century, or advances in computing and communications technology in the 1950s and 1960s, for example, went hand in hand with strong, broadly shared economic growth in the US and other developed economies.

But, in later decades, something in this relationship began to break down. Since the 1980s, we’ve seen the robotics revolution in manufacturing; the rise of software in everything; the consumer internet and the internet of things; and the growth of artificial intelligence. But during this time trend GDP growth in the US has slowed, inequality has risen and many workers — particularly, men without college degrees — have seen their real earnings fall sharply.

Globalisation and the decline of unions have played a part. But so has technological job disruption. That issue is beginning to get serious attention in Washington. In particular, politicians and policymakers are homing in on the work of MIT professor Daron Acemoglu, whose research shows that mass automation is no longer a win-win for both capital and labour. He testified at a select committee hearing to the US House of Representatives in November that automation — the substitution of machines and algorithms for tasks previously performed by workers — is responsible for 50-70 per cent of the economic disparities experienced between 1980 and 2016.

Why is this happening? Basically, while the automation of the early 20th century and the post-1945 period “increased worker productivity in a diverse set of industries and created myriad opportunities for them”, as Acemoglu said in his testimony, “what we’ve experienced since the mid 1980s is an acceleration in automation and a very sharp deceleration in the introduction of new tasks”. Put simply, he added, “the technological portfolio of the American economy has become much less balanced, and in a way that is highly detrimental to workers and especially low-education workers.”

What’s more, some things we are automating these days aren’t so economically beneficial. Consider those annoying computerised checkout stations in drug stores and groceries that force you to self-scan your purchases. They may save retailers a bit in labour costs, but they are hardly the productivity enhancer of, say, a self-driving combine harvester. Cecilia Rouse, chair of the White House’s Council of Economic Advisers, spoke for many when she told a Council on Foreign Relations event that she’d rather “stand in line [at the pharmacy] so that someone else has a job — it may not be a great job, but it is a job — and where I actually feel like I get better assistance.”

Still, there’s no holding back technology. The question is how to make sure more workers can capture its benefits. In her “Virtual Davos” speech a couple of weeks ago, Treasury secretary Janet Yellen pointed out that recent technologically driven productivity gains might exacerbate rather than mitigate inequality. She pointed to the fact that, while the “pandemic-induced surge in telework” will ultimately raise US productivity by 2.7 per cent, the gains will accrue mostly to upper income, white-collar workers, just as online learning has been better accessed and leveraged by wealthier, white students.

Education is where the rubber meets the road in fixing technology-driven inequality. As Harvard researchers Claudia Goldin and Laurence Katz have shown, when the relationship between education and technology gains breaks down, tech-driven prosperity is no longer as widely shared. This is why the Biden administration has been pushing investments into community college, apprenticeships and worker training…(More)”.

The emergence of algorithmic solidarity: unveiling mutual aid practices and resistance among Chinese delivery workers

Paper by Zizheng Yu, Emiliano Treré, and Tiziano Bonini: “This study explores how Chinese riders game the algorithm-mediated governing system of food delivery service platforms and how they mobilize WeChat to build solidarity networks to assist each other and better cope with the platform economy. We rely on 12 interviews with Chinese riders from 4 platforms (Meituan, Eleme, SF Express and Flash EX) in 5 cities, and draw on a 4-month online observation of 7 private WeChat groups. The article provides a detailed account of the gamification ranking and competition techniques employed by delivery platforms to drive the riders to achieve efficiency and productivity gains. Then, it critically explores how Chinese riders adapt and react to the algorithmic systems that govern their work by setting up private WeChat groups and developing everyday practices of resilience and resistance. This study demonstrates that Chinese riders working for food delivery platforms incessantly create a complex repertoire of tactics and develop hidden transcripts to resist the algorithmic control of digital platforms….(More)”.

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

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

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

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

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

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?

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)”.