Your Boss Is an Algorithm: Artificial Intelligence, Platform Work and Labour


Book by Antonio Aloisi and Valerio De Stefano: “What effect do robots, algorithms, and online platforms have on the world of work? Using case studies and examples from across the EU, the UK, and the US, this book provides a compass to navigate this technological transformation as well as the regulatory options available, and proposes a new map for the era of radical digital advancements.

From platform work to the gig-economy and the impact of artificial intelligence, algorithmic management, and digital surveillance on workplaces, technology has overwhelming consequences for everyone’s lives, reshaping the labour market and straining social institutions. Contrary to preliminary analyses forecasting the threat of human work obsolescence, the book demonstrates that digital tools are more likely to replace managerial roles and intensify organisational processes in workplaces, rather than opening the way for mass job displacement.

Can flexibility and protection be reconciled so that legal frameworks uphold innovation? How can we address the pervasive power of AI-enabled monitoring? How likely is it that the gig-economy model will emerge as a new organisational paradigm across sectors? And what can social partners and political players do to adopt effective regulation?

Technology is never neutral. It can and must be governed, to ensure that progress favours the many. Digital transformation can be an essential ally, from the warehouse to the office, but it must be tested in terms of social and political sustainability, not only through the lenses of economic convenience. Your Boss Is an Algorithm offers a guide to explore these new scenarios, their promises, and perils…(More)”

Stories to Work By


Essay by William E. Spriggs: “In Charlie Chaplin’s 1936 film Modern Times, humans in a factory are reduced to adjuncts to a massive series of cogs and belts. Overlords bark commands from afar to a servant class, and Chaplin’s hapless hero is literally consumed by the machine … and then spit out by it. In the film, the bosses have all the power, and machines keep workers in check.

Modern Times’s dystopian narrative remains with us today. In particular, it is still held by many policymakers who assume that increasing technological progress, whether mechanical or informational, inevitably means that ordinary workers will lose. This view perpetuates itself when policies that could give workers more power in times of technological change are overlooked, while those that disempower workers are adopted. If we are to truly consider science policy for the future, we need to understand how this narrative about workers and technology functions, where it is misleading, and how deliberate policies can build a better world for all….

Today’s tales of pending technological dystopia—echoed in economics papers as well as in movies and news reports—blind us to the lessons we could glean from the massive disruptions of earlier periods of even greater change. Today the threat of AI is portrayed as revolutionary, and previous technological change as slow and inconsequential—but this was never the case. These narratives of technological inevitability limit the tools we have at our disposal to promote equality and opportunity.

The challenges we face today are far from insurmountable: technology is not destiny. Workers are not doomed to be Chaplin’s victim of technology with one toe caught in the gears of progress. We have choices, and the central challenge of science and technology policy for the next century will be confronting those choices head on. Policymakers should focus on the fundamental tasks of shaping how technology is deployed and enacting the economic rules we need to ensure that technology works for us all, rather than only the few….(More)”.

Tech Inclusion for Excluded Communities


Essay by  Linda Jakob Sadeh & Smadar Nehab: “Companies often offer practical trainings to address the problem of diversity in high tech, acknowledging the disadvantages that members of excluded communities face and trying to level the playing field in terms of expertise and skills. But such trainings often fail in generating mass participation among excluded communities in tech professions. Beyond the professional knowledge and hands-on technical experience that these trainings provide, the fundamental social, ethnic, and economic barriers often remain unaddressed.

Thus, a paradoxical situation arises: On the one hand, certain communities are excluded from high tech and from the social mobility it affords. On the other hand, even when well-meaning companies wish to hire from these communities and implement diversity and inclusion measures that should make doing so possible, the pool of qualified and interested candidates often remains small. Members of the excluded communities remain discouraged from studying or training for these professions and from joining economic growth sectors, particularly high tech.

Tech Inclusion, the model we advance in this article, seeks to untangle this paradox. It takes a sincere look at the social and economic barriers that prevent excluded communities from participating in the tech industry. It suggests that the technology industry can be a driving force for inclusion if we turn the inclusion paradigm on its head, by bringing the industry to the excluded community, instead of trying to bring the excluded community to the industry, while cultivating a supportive environment for both potential candidates and firms…(More)”.

Data for an Inclusive Economic Recovery


Report by the National Skills Coalition: “A truly inclusive economic recovery means that the workers and businesses who were most impacted by this pandemic, as well as workers who have been held back by structural barriers of discrimination or lack of opportunity, are empowered to equitably participate in and benefit from the economy’s expansion and restructuring. 

But we need data on how different workers and businesses are faring in the recovery, so 

we can hold policymakers accountable to equitable outcomes. Disparities and inequities in skills training programs can only be eliminated if there is high-quality information on program outcomes available to practitioners and policymakers to assess and address equity gaps. Once we have the data – we can use it to drive the change we need! 

 Data for an Inclusive Economic Recovery provides recommendations on how to measure and report on what really matters to help diminish structural inequities and to shape implementation of federal recovery investments as well as new state and federal workforce investments…  

Recommendations Include: 

  • Requiring that all education and skills training programs include collection of self-reported demographic characteristics of workers and learners so outcomes can be disaggregated by race, ethnicity, gender, English language proficiency, income, and geography ;
  • Ensuring participants of skills training programs know what demographic characteristics are being collected about them, who will have access to personally identifiable information, and how their data will be used; 
  • Establishing common outcomes metrics across federal skills training programs;
  • Expanding outcomes to include those that allow policymakers to assess the quality of skills training programs and measure economic mobility along a career pathway; 
  • Ensuring equitable access to administrative data; 
  • Mandating public reporting on skills training and workforce investment outcomes; and

Providing sufficient funding for linked education and workforce data systems…(More)”.

The Labor Market Impacts of Technological Change: From Unbridled Enthusiasm to Qualified Optimism to Vast Uncertainty


NBER Working Paper by David Autor: “This review considers the evolution of economic thinking on the relationship between digital technology and inequality across four decades, encompassing four related but intellectually distinct paradigms, which I refer to as the education race, the task polarization model, the automation-reinstatement race, and the era of Artificial Intelligence uncertainty. The nuance of economic understanding has improved across these epochs. Yet, traditional economic optimism about the beneficent effects of technology for productivity and welfare has eroded as understanding has advanced. Given this intellectual trajectory, it would be natural to forecast an even darker horizon ahead. I refrain from doing so because forecasting the “consequences” of technological change treats the future as a fate to be divined rather than an expedition to be undertaken. I conclude by discussing opportunities and challenges that we collectively face in shaping this future….(More)”.

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)”.