At the dawn of the digital revolution, economists questioned whether or not we were entering the next industrial revolution. They forecasted dramatic leaps in our personal productivity, automation of mundane tasks, changes in the workforce, globalization, and self-employment. These are many of the same predictions being made today as artificial intelligence lays on the horizon.
From the summary:
There is a well-documented, persistent, and growing racial wealth gap between African American families and white families in the United States. Studies indicate the median white family in the United States holds more than ten times the wealth of the median African American family.
Apart from its obvious negative impact on African American individuals, families, and communities, the racial wealth gap constrains the entire US economy. In a previous report, we projected that closing the racial wealth gap could net the US economy between $1.1 trillion and $1.5 trillion by 2028.
Despite this, the racial wealth gap threatens to grow as norms, standards, and opportunities in the current US workplace change and exacerbate existing income disparities. One critical disrupter will be the adoption of automation and other digital technologies by companies worldwide. According to estimates from the McKinsey Global Institute, companies have already invested between $20 billion and $30 billion in artificial intelligence technologies and applications. End users, businesses, and economies are hoping to significantly increase their productivity and capacity for innovation through using such technologies.
The economic impact of closing the racial wealth gap
Source: Nick Noel, Duwain Pinder, Shelley Stewart III, and Jason Wright, McKinsey, August 2019
From the summary:
The persistent racial wealth gap in the United States is a burden on black Americans as well as the overall economy. New research quantifies the impact of closing the gap and identifies key sources of this socioeconomic inequity.
The United States has spent the past century expanding its economic power, and it shows in American families’ wealth. Despite income stagnation outside the circle of high earners, median family wealth grew from $83,000 in 1992 to $97,000 in 2016 (in 2016 dollars).
Beyond the overall growth in top-line numbers, however, the growth in household wealth (defined as net worth—the net value of each family’s liquid and illiquid assets and debts) has not been inclusive. In wealth, black individuals, families, and communities tend to lag behind their white counterparts. Indeed, the median white family had more than ten times the wealth of the median black family in 2016 (Exhibit 1). In fact, the racial wealth gap between black and white families grew from about $100,000 in 1992 to $154,000 in 2016, in part because white families gained significantly more wealth (with the median increasing by $54,000), while median wealth for black families did not grow at all in real terms over that period…..
Technological efficiencies will result in the biggest reduction in headcount across the U.S. banking industry in its history, with an estimated 200,000 job cuts over the next decade, Wells Fargo & Co. said in a report. The $150 billion annually that the country’s finance firms are spending on tech — more than any other industry — will lead to lower costs, with employee compensation accounting for half of all bank expenses, said Mike Mayo, a senior analyst at Wells Fargo Securities LLC. Back office, bank branch, call center and corporate employees are being cut by about a fifth to a third, with jobs related to tech, sales, advising and consulting less affected, according to the study…..
Tech forecast to destroy more than 200,000 US bank jobs
Source: Financial Times, October 2019
US banks will cut more than 200,000 jobs in the next decade as robots and other technology bring about the “greatest transfer from labour to capital” the industry has seen, a report by analysts at Wells Fargo claims. …. Individual banks have predicted that machines could replace thousands of jobs, most notably Citigroup chief executive Mike Corbat, who said “tens of thousands” of call centre workers could be replaced, and former Deutsche Bank boss John Cryan who warned in 2017 that as many as half the bank’s 97,000-strong workforce could go. ….
….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…..
Source: Justin B. Bullock, The American Review of Public Administration, Volume 49 Issue 7, October 2019
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.
….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. ….
….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…..
Source: Lorene D. Park, Labor Law Journal, Vol. 70 no. 2, Summer 2019
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.
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.
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.