The IPL newsletter: Volume 20, Issue 402

Digital Identification: A Key to Inclusive Growth

McKinsey Global Institute
Digital identification, or “digital ID,” can be authenticated unambiguously through a digital channel, unlocking access to banking, government benefits, education, and many other critical services. The risks and potential for misuse of digital ID are real and deserve careful attention. When well-designed, digital ID not only enables civic and social empowerment, but also makes possible real and inclusive economic gains—a less well understood aspect of the technology. This report develops a framework to understand the potential economic impact of digital ID, informed by an analysis of nearly 100 ways in which digital ID can be used in Brazil, China, Ethiopia, India, Nigeria, the United Kingdom, and the United States. In these seven focus countries, extending full digital ID coverage could unlock economic value equivalent to 3 to 13 percent of GDP in 2030, with just over half of the potential economic value potentially accruing to individuals. Realizing this value is by no means certain or automatic—it necessitates multiple high-value use cases and high levels of usage—and not all of these potential sources of economic value may translate into GDP. Yet, with careful system design and policies to promote uptake and mitigate risks, digital ID could be a powerful key to inclusive growth, offering quantifiable economic value to individuals, beyond significant non-economic benefits.

Confronting the Risks of Artificial Intelligence

McKinsey Global Institute
Artificial intelligence (AI) is proving to be a double-edged sword. While this can be said of most new technologies, both sides of the AI blade are far sharper, and neither is well understood. Even as AI generates consumer benefits and business value, it is also giving rise to a host of unwanted, and sometimes serious, consequences. And while the focus of this article is AI, these knock-on effects (and the ways to prevent or mitigate them) apply equally to all advanced analytics. The most visible ones, which include privacy violations, discrimination, accidents, and manipulation of political systems, are more than enough to prompt caution. More concerning still are the consequences not yet known or experienced. Disastrous repercussions—including the loss of human life, if an AI medical algorithm goes wrong, or the compromise of national security, if an adversary feeds disinformation to a military AI system—are possible, and so are significant challenges for organizations, from reputational damage and revenue losses to regulatory backlash, criminal investigation, and diminished public trust. Leaders hoping to avoid, or at least mitigate, unintended consequences need both to build their pattern-recognition skills with respect to AI risks and to engage the entire organization so that it is ready to embrace the power and the responsibility associated with AI. The level of effort required to identify and control for all key risks dramatically exceeds prevailing norms in most organizations. Making real progress demands a multidisciplinary approach involving leaders in the C-suite and across the company; experts in areas ranging from legal and risk to IT, security, and analytics; and managers who can ensure vigilance at the front lines. This article seeks to help by first illustrating a range of easy-to-overlook pitfalls. It then presents frameworks that will assist leaders in identifying their greatest risks and implementing the breadth and depth of nuanced controls required to sidestep them. Finally, it provides an early glimpse of some real-world efforts that are currently under way to tackle AI risks through the application of these approaches.

Automation and a Changing Economy

Aspen Institute
Automation is an important ingredient driving economic growth and progress. Automation has enabled workers to transition from subsistence farming to new forms of work. Automation helped moved us from a craft system to mass production, from blue-collar to white-collar to “new collar” work—with better work, higher wages, more jobs, and better living standards. But without adequate policies and institutions, automation can also have negative effects on individuals and communities. Emerging technologies—including artificial intelligence, machine learning, and advanced robotics—have the potential to automate many tasks currently performed by workers, leading to renewed questions over what the future holds for the American workforce. Public policy must ensure the proper support structures are in place to promote opportunity and prosperity for all.

Artificial intelligence and other new technologies may lead to deeper, faster, broader, and more disruptive automation. 

Automation need not be any more disruptive in the future than it has been in the past to warrant increased policy intervention. But there are several reasons to believe that certain converging technologies—particularly artificial intelligence, advanced robotics, machine learning, and expanding computing power and data storage—could lead to increased automation disruption.

• Deeper: Recent advances in artificial learning technologies may dramatically expand the types of tasks that can be automated.
• Faster: As artificial learning technologies enable machines to learn at a faster rate, and as innovation moves from hardware to software, labor market disruptions could occur more quickly.
• Broader: As digital technologies are increasingly incorporated across a broad array of industries and jobs, disruptions may happen simultaneously, leaving few stable occupations for disrupted workers.
• More Disruptive: Recent forms of automation, such as the use of robotics, focus more on replacing labor rather than augmenting it. In addition, automation has increasingly occurred during recessions, making it harder for disrupted workers
  to find new jobs.

Part II of this report outlines a program to address automation’s challenges and opportunities. The paper calls for an all-of-the-above approach, from targeted interventions to those with systems-level impact; from place-based policies to national-level reforms to social safety net programs. The paper identifies 22 solutions to address four overarching objectives:

I. Encourage Employers to Lead a Human-Centric Approach to Automation 

Challenge: Automation changes workforce skill needs, yet employer investment in workforce development has declined.

Solutions:

•  1.  Create a Worker Training Tax Credit to encourage business investment in employees.
•  2.  Encourage employers and educational institutions to expand apprenticeships.
•  3.  Support development of industry and sector partnerships to create regional jobs and career pathways.

Challenge: Employers are making decisions about adopting automation, but may not take into account potential impacts on workers and communities.

Solutions:

•  4.  Encourage employers to engage workers in automation decision-making by promoting worker voice.
•  5.  Plan ahead to mitigate disruption and manage workforce transition.
•  6.  Promote broadly-shared prosperity through worker ownership.

II. Enable Workers to Access Skills Training, Good Jobs, and New Economic Opportunities

Challenge: The labor market is constantly evolving, with automation contributing to changing jobs and skill needs, but supports for worker training and adult education are limited.

Solutions:

•  7.  Create worker-owned Lifelong Learning and Training Accounts.
•  8.  Reform Pell Grants to include short-term training programs.
•  9.  Improve training quality through community colleges investments and income share agreements.
•  10.  Increase transparency of training outcomes through better evaluation data.
•  11.  Expand access to high quality and cost-saving career counseling.

Challenge: Many workers struggle to make ends meet, and while automation has the potential to improve job quality, it also may lead to more low-wage jobs and greater economic insecurity.

Solutions:

•  12.  Raise worker income and promote job quality through wage subsidies and the minimum wage.
•  13.  Create a more flexible labor market and promote entrepreneurship.

III. Help People and Communities Recover from Displacements

Challenge: Workers displaced by automation face significant economic challenges.

Solutions:

•  14.  Support displaced workers to retrain for in-demand fields.
•  15.  Provide wage insurance to older workers.
•  16.  Help displaced workers meet the costs associated with finding a new job..
•  17.  Modernize Unemployment Insurance to improve coverage and sustainability.
•  18.  Reduce stigma associated with career transitions.

Challenge: Communities that are severely impacted by automation require targeted and comprehensive strategies to recover and transition.

Solution:

•  19.  Improve efforts to support local economies and promote regional competitiveness.

IV. Understand the Impact of Automation on the Workforce

Challenge: Policymakers, communities, workers, businesses, educators, and other stakeholders struggle to understand how automation is changing the economy because federal, state and local data on the impact of technology on work is inadequate.

Solutions:

•  20.  Create new metrics for tracking technological progress and automation.
•  21.  Improve occupational projections to better account for automation.
•  22.  Develop better data on how local and regional labor markets are changing.

Technology is not destiny—the impact of innovation on the American worker is mediated by policy choices and how institutions, such as employers, worker organizations, nonprofits and philanthropies, respond to these challenges. By helping workers take advantage of new opportunities and assisting workers who are acutely impacted by automation return to stable work, we can promote greater opportunity and broadly shared prosperity for all.