Work and meaning in the age of AI


Report by Daniel Susskind: “It is often said that work is not only a source of income but also of meaning. In this paper, I explore the theoretical and empirical literature that addresses this relationship between work and meaning. I show that the relationship is far less clear than is commonly supposed: There is a great heterogeneity in its nature, both among today’s workers and workers over time. I explain why this relationship matters for policymakers and economists concerned about the impact of technology on work. In the short term, it is important for predicting labour market outcomes of interest. It also matters for understanding how artificial intelligence (AI) affects not only the quantity of work but its quality as well: These new technologies may erode the meaning that people get from their work. In the medium term, if jobs are lost, this relationship also matters for designing bold policy interventions like the ‘Universal Basic Income’ and ‘Job Guarantee Schemes’: Their design, and any choice between them, is heavily dependent on policymakers’—often tacit—assumptions about the nature of this underlying relationship between work and meaning. For instance, policymakers must decide whether to simply focus on replacing lost income alone (as with a Universal Basic Income) or, if they believe that work is an important and non-substitutable source of meaning, on protecting jobs for that additional role as well (as with a Job Guarantee Scheme). In closing, I explore the challenge that the age of AI presents for an important feature of liberal political theory: the idea of ‘neutrality.’..(More)”

Human-AI Teaming


Report by the National Academies of Sciences, Engineering, and Medicine: “Although artificial intelligence (AI) has many potential benefits, it has also been shown to suffer from a number of challenges for successful performance in complex real-world environments such as military operations, including brittleness, perceptual limitations, hidden biases, and lack of a model of causation important for understanding and predicting future events. These limitations mean that AI will remain inadequate for operating on its own in many complex and novel situations for the foreseeable future, and that AI will need to be carefully managed by humans to achieve their desired utility.

Human-AI Teaming: State-of-the-Art and Research Needs examines the factors that are relevant to the design and implementation of AI systems with respect to human operations. This report provides an overview of the state of research on human-AI teaming to determine gaps and future research priorities and explores critical human-systems integration issues for achieving optimal performance…(More)”

Which Connections Really Help You Find a Job?


Article by Iavor Bojinov, Karthik Rajkumar, Guillaume Saint-Jacques, Erik Brynjolfsson, and Sinan Aral: “Whom should you connect with the next time you’re looking for a job? To answer this question, we analyzed data from multiple large-scale randomized experiments involving 20 million people to measure how different types of connections impact job mobility. Our results, published recently in Science Magazine, show that your strongest ties — namely your connections to immediate coworkers, close friends, and family — were actually the least helpful for finding new opportunities and securing a job. You’ll have better luck with your weak ties: the more infrequent, arm’s-length relationships with acquaintances.

To be more specific, the ties that are most helpful for finding new jobs tend to be moderately weak: They strike a balance between exposing you to new social circles and information and having enough familiarity and overlapping interests so that the information is useful. Our findings uncovered the relationship between the strength of the connection (as measured by the number of mutual connections prior to connecting) and the likelihood that a job seeker transitions to a new role within the organization of a connection.The observation that weak ties are more beneficial for finding a job is not new. Sociologist Mark Granovetter first laid out this idea in a seminal 1973 paper that described how a person’s network affects their job prospects. Since then, the theory, known as the “strength of weak ties,” has become one of the most influential in the social sciences — underpinning network theories of information diffusion, industry structure, and human cooperation….(More)”.

The Labor Market Consequences of Appropriate Technology


Paper by Gustavo de Souza: “Developing countries rely on technology created by developed countries. This paper demonstrates that such reliance increases wage inequality but leads to greater production in developing countries. I study a Brazilian innovation program that taxed the leasing of international technology to subsidize national innovation. I show that the program led firms to replace technology licensed from developed countries with in-house innovations, which led to a decline in both employment and the share of high-skilled workers. Using a model of directed technological change and technology transfer, I find that increasing the share of firms that patent in Brazil by 1 p.p. decreases the skilled wage premium by 0.02% and production by 0.2%…(More)”.

Does AI Debias Recruitment? Race, Gender, and AI’s “Eradication of Difference”


Paper by Eleanor Drage & Kerry Mackereth: “In this paper, we analyze two key claims offered by recruitment AI companies in relation to the development and deployment of AI-powered HR tools: (1) recruitment AI can objectively assess candidates by removing gender and race from their systems, and (2) this removal of gender and race will make recruitment fairer, help customers attain their DEI goals, and lay the foundations for a truly meritocratic culture to thrive within an organization. We argue that these claims are misleading for four reasons: First, attempts to “strip” gender and race from AI systems often misunderstand what gender and race are, casting them as isolatable attributes rather than broader systems of power. Second, the attempted outsourcing of “diversity work” to AI-powered hiring tools may unintentionally entrench cultures of inequality and discrimination by failing to address the systemic problems within organizations. Third, AI hiring tools’ supposedly neutral assessment of candidates’ traits belie the power relationship between the observer and the observed. Specifically, the racialized history of character analysis and its associated processes of classification and categorization play into longer histories of taxonomical sorting and reflect the current demands and desires of the job market, even when not explicitly conducted along the lines of gender and race. Fourth, recruitment AI tools help produce the “ideal candidate” that they supposedly identify through by constructing associations between words and people’s bodies. From these four conclusions outlined above, we offer three key recommendations to AI HR firms, their customers, and policy makers going forward…(More)”.

Working with AI: Real Stories of Human-Machine Collaboration


Book by Thomas H. Davenport and Steven M. Miller: “This book breaks through both the hype and the doom-and-gloom surrounding automation and the deployment of artificial intelligence-enabled—“smart”—systems at work. Management and technology experts Thomas Davenport and Steven Miller show that, contrary to widespread predictions, prescriptions, and denunciations, AI is not primarily a job destroyer. Rather, AI changes the way we work—by taking over some tasks but not entire jobs, freeing people to do other, more important and more challenging work. By offering detailed, real-world case studies of AI-augmented jobs in settings that range from finance to the factory floor, Davenport and Miller also show that AI in the workplace is not the stuff of futuristic speculation. It is happening now to many companies and workers.These cases include a digital system for life insurance underwriting that analyzes applications and third-party data in real time, allowing human underwriters to focus on more complex cases; an intelligent telemedicine platform with a chat-based interface; a machine learning-system that identifies impending train maintenance issues by analyzing diesel fuel samples; and Flippy, a robotic assistant for fast food preparation. For each one, Davenport and Miller describe in detail the work context for the system, interviewing job incumbents, managers, and technology vendors. Short “insight” chapters draw out common themes and consider the implications of human collaboration with smart systems…(More)”.

A Massive LinkedIn Study Reveals Who Actually Helps You Get That Job


Article by Viviane Callier : “If you want a new job, don’t just rely on friends or family. According to one of the most influential theories in social science, you’re more likely to nab a new position through your “weak ties,” loose acquaintances with whom you have few mutual connections. Sociologist Mark Granovetter first laid out this idea in a 1973 paper that has garnered more than 65,000 citations. But the theory, dubbed “the strength of weak ties,” after the title of Granovetter’s study, lacked causal evidence for decades. Now a sweeping study that looked at more than 20 million people on the professional social networking site LinkedIn over a five-year period finally shows that forging weak ties does indeed help people get new jobs. And it reveals which types of connections are most important for job hunters…Along with job seekers, policy makers could also learn from the new paper. “One thing the study highlights is the degree to which algorithms are guiding fundamental, baseline, important outcomes, like employment and unemployment,” Aral says. The role that LinkedIn’s People You May Know function plays in gaining a new job demonstrates “the tremendous leverage that algorithms have on employment and probably other factors of the economy as well.” It also suggests that such algorithms could create bellwethers for economic changes: in the same way that the Federal Reserve looks at the Consumer Price Index to decide whether to hike interest rates, Aral suggests, networks such as LinkedIn might provide new data sources to help policy makers parse what is happening in the economy. “I think these digital platforms are going to be an important source of that,” he says…(More)”

The New ADP National Employment Report


Press Release: “The new ADP National Employment Report (NER) launched today in collaboration with the Stanford Digital Economy Lab. Earlier this spring, the ADP Research Institute paused the NER in order to refine the methodology and design of the report. Part of that evolution was teaming up data scientists at the Stanford Digital Economy Lab to add a new perspective and rigor to the report. The new report uses fine-grained, high-frequency data on jobs and wages to deliver a richer and more useful analysis of the labor market.

Let’s take a look at some of the key changes with the new NER, along with the new ADP® Pay Insights Report.

It’s independent. The key change is that the new ADP NER is an independent measure of the US labor market, rather than a forecast of the BLS monthly jobs number. Jobs report and pay insights are based on anonymized and aggregated payroll data from more than 25 million US employees across 500,000 companies. The new report focuses solely on ADP’s clients and private-sector change…(More)”.

Reorganise: 15 stories of workers fighting back in a digital age 


Book edited by Hannah O’Rourke & Edward Saperia: “In only a decade, the labour market has changed beyond all recognition – from zero-hour contracts to platform monopolies. As capitalism has re-created itself for the digital age, so too must the workers whose labour underpins it.

From a union for instagram influencers to roadworkers organising through a Facebook Group, former WSJ journalist Lucy Harley-McKeown takes us on a journey to discover how workers are fighting back in the 21st century…(More)”.

The fear of technology-driven unemployment and its empirical base


Article by Kerstin Hötte, Melline Somers and Angelos Theodorakopoulos:”New technologies may replace human labour, but can simultaneously create jobs if workers are needed to use these technologies or if new economic activities emerge. At the same time, technology-driven productivity growth may increase disposable income, stimulating a demand-induced employment expansion. Based on a systematic review of the empirical literature on technological change and its impact on employment published in the past four decades, this column suggests that the empirical support for the labour-creating effects of technological change dominates that for labour-replacement…(More)”.