Category Archives: Automation & Artificial Intelligence

AFL-CIO Commission on the Future of Work and Unions

Source: AFL-CIO Commission on the Future of Work and Unions, Report to the AFL-CIO General Board, September 2019

….We present this report with fresh optimism that working people can and will build a future of work that works for all of us. But getting the job done requires more than engaging with innovation in the workplace. We must innovate ourselves, strengthen our unions, organize new ones and bring more workers into our ranks. The stakes are enormous. A system that fails to provide a decent standard of living for its people will not stand. So if technology and public policy continue to be used to further concentrate economic power in the hands of the wealthy few, our system of government and our way of life are in grave danger. But it doesn’t have to be that way. The labor movement can be inclusive enough and strong enough to raise living standards across the economy and ensure good jobs for everyone who wants to work.

This report is our plan to make that happen…..

Artificial Intelligence, Discretion, and Bureaucracy

Source: Justin B. Bullock, The American Review of Public Administration, Volume 49 Issue 7, October 2019
(subscription required)

From the abstract:
This essay highlights the increasing use of artificial intelligence (AI) in governance and society and explores the relationship between AI, discretion, and bureaucracy. AI is an advanced information communication technology tool (ICT) that changes both the nature of human discretion within a bureaucracy and the structure of bureaucracies. To better understand this relationship, AI, discretion, and bureaucracy are explored in some detail. It is argued that discretion and decision-making are strongly influenced by intelligence, and that improvements in intelligence, such as those that can be found within the field of AI, can help improve the overall quality of administration. Furthermore, the characteristics, strengths, and weaknesses of both human discretion and AI are explored. Once these characteristics are laid out, a further exploration of the role AI may play in bureaucracies and bureaucratic structure is presented, followed by a specific focus on systems-level bureaucracies. In addition, it is argued that task distribution and task characteristics play a large role, along with the organizational and legal context, in whether a task favors human discretion or the use of AI. Complexity and uncertainty are presented as the major defining characteristics for categorizing tasks. Finally, a discussion is provided about the important cautions and concerns of utilizing AI in governance, in particular, with respect to existential risk and administrative evil.

Three Big Ideas for a Future of Less Work and a Three-Dimensional Alternative

Source: Cynthia Estlund, Law and Contemporary Problems, Vol. 82 no. 3, 2019

….At the same time, each of those three big ideas holds within it an essential component of a sound three dimensional response to the uncertain but real prospect of job losses. In lieu of UBI [universal basic income], we should expand universal social benefits—starting with health care and higher education—and income support for the working and non-working poor. In lieu of a federal job guarantee, we should ramp up public investments in infrastructure, social and community services, and early education, all of which would address unmet societal needs while creating decent jobs. And in lieu of (or at least before) reducing weekly hours of work across the board, we should expand access to paid leaves, holidays, and vacations, as well as voluntary part-time work and retirement security; we could thereby spread work and meet varied individual needs and preferences through days, weeks, months, and years of time off.

In combination, these three interventions—expanded universal social benefits and income support, public investments in physical and social infrastructure and the job creation those will entail, and wider access to paid leaves and respites from work—would advance core objectives of each of the three big ideas while muting their disadvantages. Together they would both cushion and offset automation-related job losses, while spreading the work that remains and maintaining or boosting incomes. This trio of policies could and should also be funded in a way that helps to redistribute income from the top to the bottom of an egregiously and increasingly lopsided income distribution.

…..In what follows, I will fill in the outlines of this argument. Part II will briefly set out some normative priors about the multiple ends we should be pursuing as we face a future of less work. A long Part III will take up each of the Three Big Ideas, briefly tracing their genealogy and identifying some strengths and weaknesses of each. Part IV will return to the core aspirations of the Three Big Ideas, and sketch a combination of the three – a three-dimensional strategy – that can preserve much of the good while avoiding much that is problematic in the more single-minded Three Big Ideas. ….

The Work of the Future: Shaping Technology and Institutions

Source: MIT Task Force on the Work of the Future, Fall 2019

….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 nav-igate 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. We begin in Section 1 by stating an underlying premise of our project: work is intrinsically valuable to individuals and to society as a whole, and we should seek to improve rather than eliminate it. The second section introduces the broader concerns that motivated the Task Force’s formation. Here we address a paradox: despite a decade of low unemployment and generally rising prosperity in the United States and industrialized countries, public discourse around the subject of technology and work is deeply pessimistic. We argue that this pessimism is neither misguided nor uninformed, but rather a reflection of a decades-long disconnect between rising productivity and stagnant incomes for the majority of workers…..

L&E Evolution Part III: Managing Employees in a Digital Age

Source: Lorene D. Park, Labor Law Journal, Vol. 70 no. 2, Summer 2019
(subscription required)

The expectation that business will be done electronically, the trend toward paperless records, and ongoing advances in technology have birthed so many legal issues that, for employers, compliance may seem impossible.

For example, much was made of the promise of using artificial intelligence to screen job applicants, but it emerged that AI can both learn and perpetuate human biases that may violate Title VII. And using online employment agreements also may result in litigation over whether an employee “clicked” on a screen to agree.

What follows is an overview of key issues that have emerged for employers, organized around the lifespan of an employment relationship. We’ll start with the hiring process, covering accessibility, screening methods, electronic agreements, and more. Then we’ll cover computer use policies, trade secrets, wiretapping and electronic privacy statutes, data breach notification, social media, NLRA protections, and other issues that arise during the employment relationship. We’ll wrap with a discussion on privacy, including surveillance of employees, as well as issues surrounding termination of employment.

Artificial intelligence: Implications for the future of work

Source: John Howard, American Journal of Industrial Medicine, Early View, August 22, 2019

From the abstract:
Artificial intelligence (AI) is a broad transdisciplinary field with roots in logic, statistics, cognitive psychology, decision theory, neuroscience, linguistics, cybernetics, and computer engineering. The modern field of AI began at a small summer workshop at Dartmouth College in 1956. Since then, AI applications made possible by machine learning (ML), an AI subdiscipline, include Internet searches, e‐commerce sites, goods and services recommender systems, image and speech recognition, sensor technologies, robotic devices, and cognitive decision support systems (DSSs). As more applications are integrated into everyday life, AI is predicted to have a globally transformative influence on economic and social structures similar to the effect that other general‐purpose technologies, such as steam engines, railroads, electricity, electronics, and the Internet, have had. Novel AI applications in the workplace of the future raise important issues for occupational safety and health. This commentary reviews the origins of AI, use of ML methods, and emerging AI applications embedded in physical objects like sensor technologies, robotic devices, or operationalized in intelligent DSSs. Selected implications on the future of work arising from the use of AI applications, including job displacement from automation and management of human‐machine interactions, are also reviewed. Engaging in strategic foresight about AI workplace applications will shift occupational research and practice from a reactive posture to a proactive one. Understanding the possibilities and challenges of AI for the future of work will help mitigate the unfavorable effects of AI on worker safety, health, and well‐being.

When Robots Make Legal Mistakes

Source: Susan C. Morse, Oklahoma Law Review, Vol. 72, 2019

From the abstract:
The questions presented by robots’ legal mistakes are examples of the legal process inquiry that asks when the law will accept decisions as final, even if they are mistaken. Legal decision-making robots include market robots and government robots. In either category, they can make mistakes of undercompliance or overcompliance. A market robot’s overcompliance mistake or a government robot’s undercompliance mistake is unlikely to be challenged. On the other hand, government enforcement can challenge a market robot’s undercompliance mistake, and an aggrieved regulated party can object to a government robot’s overcompliance mistake. Especially if robots cannot defend their legal decisions due to a lack of explainability, they will have an incentive to make decisions that will avoid the prospect of challenge. This incentive could encourage counterintuitive results. For instance, it could encourage market robots to overcomply and government robots to undercomply with the law.

The future of work in America: People and places, today and tomorrow

Source: Susan Lund, James Manyika, Liz Hilton Segel, André Dua, Bryan Hancock, Scott Rutherford, and Brent Macon, McKinsey Global Institute, July 2019

From the summary:

…..A new report from the McKinsey Global Institute, The future of work in America: People and places, today and tomorrow, analyzes more than 3,000 US counties and 315 cities and finds they are on sharply different paths. Automation is not happening in a vacuum, and the health of local economies today will affect their ability to adapt and thrive in the face of the changes that lie ahead.

The trends outlined in this report could widen existing disparities between high-growth cities and struggling rural areas, and between high-wage workers and everyone else. But this is not a foregone conclusion. The United States can improve outcomes nationwide by connecting displaced workers with new opportunities, equipping people with the skills they need to succeed, revitalizing distressed areas, and supporting workers in transition. Returning to more inclusive growth will require the combined energy and ingenuity of business leaders, policy makers, educators, and nonprofits across the country…..

The Invisible Web of Work: The Intertwining of A-I, Electronic Surveillance, and Labor Law

Source: Richard A. Bales, Katherine V.W. Stone, UCLA School of Law, Public Law Research Paper No. 19-18, Last revised: June 30, 2019

From the abstract:
Employers and others who hire or engage workers to perform services use a dizzying array of electronic mechanisms to make personnel decisions about hiring, worker evaluation, compensation, discipline, and retention. These electronic mechanisms include electronic trackers, surveillance cameras, metabolism monitors, wearable biological measuring devices, and implantable technology. These tools enable employers to record their workers’ every movement, listen in on their conversations, measure minute aspects of performance, and detect oppositional organizing activities. The data collected is transformed by means of artificial intelligence (A-I) algorithms into a permanent electronic resume that can identify and predict an individual’s performance as well as their work ethic, personality, union proclivity, employer loyalty, and future health care costs. The electronic resume produced by A-I will accompany workers from job to job as they move around the boundaryless workplace. Thus A-I and electronic monitoring produce an invisible electronic web that threatens to invade worker privacy, deter unionization, enable subtle forms of employer blackballing, exacerbate employment discrimination, render unions ineffective, and obliterate the protections of the labor laws.

This article describes the many ways A-I is being used in the workplace and how its use is transforming the practices of hiring, evaluating, compensating, controlling, and dismissing workers. It then focuses on four areas of law in which A-I threatens to undermine worker protections: anti-discrimination law, privacy law, antitrust law, and labor law. Finally, this article maps out an agenda for future law reform and research.

Is Technology Widening the Gender Gap? Automation and the Future of Female Employment

Source: Mariya Brussevich, Era Dabla-Norris, Salma Khalid, IMF Working Paper No. 19/91, May 2019

From the abstract:
Using individual level data on task composition at work for 30 advanced and emerging economies, we find that women, on average, perform more routine tasks than men/tasks that are more prone to automation. To quantify the impact on jobs, we relate data on task composition at work to occupation level estimates of probability of automation, controlling for a rich set of individual characteristics (e.g., education, age, literacy and numeracy skills). Our results indicate that female workers are at a significantly higher risk for displacement by automation than male workers, with 11 percent of the female workforce at high risk of being automated given the current state of technology, albeit with significant cross-country heterogeneity. The probability of automation is lower for younger cohorts of women, and for those in managerial positions.