Cognitive Science as a New People Science for the Future of Work

Brief by Frida Polli et al: “The notion of studying people in jobs as a science—in fields such as human resource management, people analytics, and industrial-organizational psychology—dates back to at least the early 20th century. In 1919, Yale psychologist Henry Charles Link wrote, “The application of science to the problem of employment is just beginning to receive serious attention,” at last providing an alternative to the “hire and fire” methods of 19th-century employers. A year later, prominent organizational theorists Ordway Teal and Henry C. Metcalf claimed, “The new focus in administration is to be the human element. The new center of attention and solicitude is the individual person, the worker.” The overall conclusion at the time was that various social and psychological factors governed differences in employee productivity and satisfaction….This Brief Proceeds in Five Sections:

● First, we review the limitations of traditional approaches to people science. In particular, we focus on four needs of the modern employer that are not satisfied by the status quo: job fit, soft skills, fairness, and flexibility.

● Second, we present the foundations of a new people science by explaining how advancements in fields like cognitive science and neuroscience can be used to understand the individual differences between humans.

● Third, we describe four best practices that should govern the application of the new people science theories to real-world employment contexts.

● Fourth, we present a case study of how one platform company has used the new people science to create hiring models for five high-growth roles.● Finally, we explain how the type of insights presented in Section IV can be made actionable in the context of retraining employees for the future of work….(More)”.

10 Questions That Will Determine the Future of Work

Article by Jeffrey Brown and Stefaan Verhulst: “…But in many cases, policymakers face a blizzard of contradictory information and forecasts that can lead to confusion and inaction. Unable to make sense of the torrent of data being thrown their way, policymakers often end up being preoccupied by the answers presented — rather than reflecting on the questions that matter.

If we want to design “good” future-of-work policies, we must have an inclusive and wide-ranging discussion of what we are trying to solve before we attempt to develop and deploy solutions….

We have found that policymakers often fail to ask questions and are often uncertain about the variables that underpin a problem.

In addition, few of the interventions that have been deployed make the best use of data, an emerging but underused asset that is increasingly available as a result of the ongoing digital transformation. If civil society, think tanks and others fail to create the space for a sustainable future-of-work policy to germinate, “solutions” without clearly articulated problems will continue to dictate policy…

Our 100 Questions Initiative seeks to interrupt this cycle of preoccupation with answers by ensuring that policymakers are, first of all, armed with a methodology they can use to ask the right questions and from there, craft the right solutions.

We are now releasing the top 10 questions and are seeking the public’s assistance through voting and providing feedback on whether or not these are really the right questions we should be asking:

Preparing for the Future of Work

  1. How can we determine the value of skills relevant to the future-of work-marketplace, and how can we increase the value of human labor in the 21st century?
  2. What are the economic and social costs and benefits of modernizing worker-support systems and providing social protection for workers of all employment backgrounds, but particularly for women and those in part-time or informal work?
  3. How does the current use of AI affect diversity and equity in the labor force? How can AI be used to increase the participation of underrepresented groups (including women, Black people, Latinx people, and low-income communities)? What aspects/strategies have proved most effective in reducing AI biases?…(More) (See also:

Court Rules Deliveroo Used ‘Discriminatory’ Algorithm

Gabriel Geiger at Motherboard: “An algorithm used by the popular European food delivery app Deliveroo to rank and offer shifts to riders is discriminatory, an Italian court ruled late last week, in what some experts are calling a historic decision for the gig economy. The case was brought by a group of Deliveroo riders backed by CGIL, Italy’s largest trade union. 

markedly detailed ordinance written by presiding judge Chiara Zompi gives an intimate look at one of many often secretive algorithms used by gig platforms to micromanage workers and which can have profound impacts on their livelihoods. 

While machine-learning algorithms are central to Deliveroo’s entire business model, the particular algorithm examined by the court allegedly was used to determine the “reliability” of a rider. According to the ordinance, if a rider failed to cancel a shift pre-booked through the app at least 24 hours before its start, their “reliability index” would be negatively affected. Since riders deemed more reliable by the algorithm were first to be offered shifts in busier timeblocks, this effectively meant that riders who can’t make their shifts—even if it’s because of a serious emergency or illness—would have fewer job opportunities in the future….(More)”

The new SkillsMatch platform tackles skills assessment and matches your skills with training

European Commission: “The European labour market requires new skills to meet the demands of the Digital Age. EU citizens should have the right training, skills and support to empower them to find quality jobs and improve their living standards.

‘Soft skills’ such as confidence, teamwork, self-motivation, networking, presentation skills, are considered important for the employability and adaptability of Europe’s citizens. Soft skills are essential for how we work together and influence the decisions we take every day and can be more important than hard skills in today’s workplaces. The lack of soft skills is often only discovered once a person is already working on the job.

The state-of-the-art SkillsMatch platform helps users to match and adapt their soft skills assets to the demands of the labour market. The project is the first to offer a fully comprehensive platform with style guide cataloguing 36 different soft skills and matching them with occupations, as well as training opportunities, offering a large number of courses to improve soft skills depending on the chosen occupation.

The platform proposes courses, such as organisation and personal development, entrepreneurship, business communication and conflict resolution. There is a choice of courses in Spanish and English. Moreover, the platform will also provide recognition of the new learning and skills (open badges)…(More)”.

Don’t Fear the Robots, and Other Lessons From a Study of the Digital Economy

Steve Lohr at the New York Times: “L. Rafael Reif, the president of Massachusetts Institute of Technology, delivered an intellectual call to arms to the university’s faculty in November 2017: Help generate insights into how advancing technology has changed and will change the work force, and what policies would create opportunity for more Americans in the digital economy.

That issue, he wrote, is the “defining challenge of our time.”

Three years later, the task force assembled to address it is publishing its wide-ranging conclusions. The 92-page report, “The Work of the Future: Building Better Jobs in an Age of Intelligent Machines,” was released on Tuesday….

Here are four of the key findings in the report:

Most American workers have fared poorly.

It’s well known that those on the top rungs of the job ladder have prospered for decades while wages for average American workers have stagnated. But the M.I.T. analysis goes further. It found, for example, that real wages for men without four-year college degrees have declined 10 to 20 percent since their peak in 1980….

Robots and A.I. are not about to deliver a jobless future.

…The M.I.T. researchers concluded that the change would be more evolutionary than revolutionary. In fact, they wrote, “we anticipate that in the next two decades, industrialized countries will have more job openings than workers to fill them.”…

Worker training in America needs to match the market.

“The key ingredient for success is public-private partnerships,” said Annette Parker, president of South Central College, a community college in Minnesota, and a member of the advisory board to the M.I.T. project.

The schools, nonprofits and corporate-sponsored programs that have succeeded in lifting people into middle-class jobs all echo her point: the need to link skills training to business demand….

Workers need more power, voice and representation.The report calls for raising the minimum wage, broadening unemployment insurance and modifying labor laws to enable collective bargaining in occupations like domestic and home-care workers and freelance workers. Such representation, the report notes, could come from traditional unions or worker advocacy groups like the National Domestic Workers Alliance, Jobs With Justice and the Freelancers Union….(More)”

The Work of the Future: Shaping Technology and Institutions

Report by David Autor, David Mindell and Elisabeth Reynolds for the MIT Future of Work Task Force: “The world now stands on the cusp of a technological revolution in artificial intelligence and robotics that may prove as transformative for economic growth and human potential as were electrification, mass production, and electronic telecommunications in their eras. New and emerging technologies will raise aggregate economic output and boost the wealth of nations. Will these developments enable people to attain higher living standards, better working conditions, greater economic security, and improved health and longevity? The answers to these questions are not predetermined. They depend upon the institutions, investments, and policies that we deploy to harness the opportunities and confront the challenges posed by this new era.

How can we move beyond unhelpful prognostications about the supposed end of work and toward insights that will enable policymakers, businesses, and people to better navigate the disruptions that are coming and underway? What lessons should we take from previous epochs of rapid technological change? How is it different this time? And how can we strengthen institutions, make investments, and forge policies to ensure that the labor market of the 21st century enables workers to contribute and succeed?

To help answer these questions, and to provide a framework for the Task Force’s efforts over the next year, this report examines several aspects of the interaction between work and technology….(More)”.

Turning Point Policymaking in the Era of Artificial Intelligence

Book by Darrell M. West and John R. Allen: “Until recently, “artificial intelligence” sounded like something out of science fiction. But the technology of artificial intelligence, AI, is becoming increasingly common, from self-driving cars to e-commerce algorithms that seem to know what you want to buy before you do. Throughout the economy and many aspects of daily life, artificial intelligence has become the transformative technology of our time.

Despite its current and potential benefits, AI is little understood by the larger public and widely feared. The rapid growth of artificial intelligence has given rise to concerns that hidden technology will create a dystopian world of increased income inequality, a total lack of privacy, and perhaps a broad threat to humanity itself.

In their compelling and readable book, two experts at Brookings discuss both the opportunities and risks posed by artificial intelligence—and how near-term policy decisions could determine whether the technology leads to utopia or dystopia.

Drawing on in-depth studies of major uses of AI, the authors detail how the technology actually works. They outline a policy and governance blueprint for gaining the benefits of artificial intelligence while minimizing its potential downsides.

The book offers major recommendations for actions that governments, businesses, and individuals can take to promote trustworthy and responsible artificial intelligence. Their recommendations include: creation of ethical principles, strengthening government oversight, defining corporate culpability, establishment of advisory boards at federal agencies, using third-party audits to reduce biases inherent in algorithms, tightening personal privacy requirements, using insurance to mitigate exposure to AI risks, broadening decision-making about AI uses and procedures, penalizing malicious uses of new technologies, and taking pro-active steps to address how artificial intelligence affects the workforce….(More)”.

Self-interest and data protection drive the adoption and moral acceptability of big data technologies: A conjoint analysis approach

Paper by Rabia I.Kodapanakka, lMark J.Brandt, Christoph Kogler, and Iljavan Beest: “Big data technologies have both benefits and costs which can influence their adoption and moral acceptability. Prior studies look at people’s evaluations in isolation without pitting costs and benefits against each other. We address this limitation with a conjoint experiment (N = 979), using six domains (criminal investigations, crime prevention, citizen scores, healthcare, banking, and employment), where we simultaneously test the relative influence of four factors: the status quo, outcome favorability, data sharing, and data protection on decisions to adopt and perceptions of moral acceptability of the technologies.

We present two key findings. (1) People adopt technologies more often when data is protected and when outcomes are favorable. They place equal or more importance on data protection in all domains except healthcare where outcome favorability has the strongest influence. (2) Data protection is the strongest driver of moral acceptability in all domains except healthcare, where the strongest driver is outcome favorability. Additionally, sharing data lowers preference for all technologies, but has a relatively smaller influence. People do not show a status quo bias in the adoption of technologies. When evaluating moral acceptability, people show a status quo bias but this is driven by the citizen scores domain. Differences across domains arise from differences in magnitude of the effects but the effects are in the same direction. Taken together, these results highlight that people are not always primarily driven by self-interest and do place importance on potential privacy violations. They also challenge the assumption that people generally prefer the status quo….(More)”.

Federal Sources of Entrepreneurship Data: A Compendium

Compendium developed by Andrew Reamer: “The E.M. Kauffman Foundation has asked the George Washington Institute of Public Policy (GWIPP) to prepare a compendium of federal sources of data on self-employment, entrepreneurship, and small business development. The Foundation believes that the availability of useful, reliable federal data on these topics would enable robust descriptions and explanations of entrepreneurship trends in the United States and so help guide the development of effective entrepreneurship policies.

Achieving these ends first requires the identification and detailed description of available federal datasets, as provided in this compendium. Its contents include:

  • An overview and discussion of 18 datasets from four federal agencies, organized by two categories and five subcategories.
  • Tables providing information on each dataset, including:
    • scope of coverage of self-employed, entrepreneurs, and businesses;
    • data collection methods (nature of data source, periodicity, sampling frame, sample size);
    • dataset variables (owner characteristics, business characteristics and operations, geographic areas);
    • Data release schedule; and
    • Data access by format (including fixed tables, interactive tools, API, FTP download, public use microdata samples [PUMS], and confidential microdata).

For each dataset, examples of studies, if any, that use the data source to describe and explain trends in entrepreneurship.
The author’s aim is for the compendium to facilitate an assessment of the strengths and weaknesses of currently available federal datasets, discussion about how data availability and value can be improved, and implementation of desired improvements…(More)”

The Neuroscience of Trust

Paul J. Zak at Harvard Business Review: “…About a decade ago, in an effort to understand how company culture affects performance, I began measuring the brain activity of people while they worked. The neuroscience experiments I have run reveal eight ways that leaders can effectively create and manage a culture of trust. I’ll describe those strategies and explain how some organizations are using them to good effect. But first, let’s look at the science behind the framework.

What’s Happening in the Brain

Back in 2001 I derived a mathematical relationship between trust and economic performance. Though my paper on this research described the social, legal, and economic environments that cause differences in trust, I couldn’t answer the most basic question: Why do two people trust each other in the first place? Experiments around the world have shown that humans are naturally inclined to trust others—but don’t always. I hypothesized that there must be a neurologic signal that indicates when we should trust someone. So I started a long-term research program to see if that was true….

How to Manage for Trust

Through the experiments and the surveys, I identified eight management behaviors that foster trust. These behaviors are measurable and can be managed to improve performance.

Recognize excellence.

The neuroscience shows that recognition has the largest effect on trust when it occurs immediately after a goal has been met, when it comes from peers, and when it’s tangible, unexpected, personal, and public. Public recognition not only uses the power of the crowd to celebrate successes, but also inspires others to aim for excellence. And it gives top performers a forum for sharing best practices, so others can learn from them….(More)”.