Educational Policy for AI and an Uncertain Labor Market

In the next few blog posts, we will present information and generate discussion on policy issues at the intersection of AI, the future of the workforce, and educational systems. Because AI technology and applications are developing at such a rapid pace, current policies will likely not be able to impact sufficiently the workforce needs even in 2024 — the time frame for middle school students to prepare for low skill jobs and for beginning college students to prepare for higher skilled work. Transparency in educational policies requires goal setting based on better data and insights into emerging technologies, likely changes in the labor market, and corresponding challenges to our educational systems. The topics and resources below will be the focus of future AI Policy posts.

Technology

IBM’s Jim Spohrer has an outstanding set of slides “A Look Toward the Future”, incorporating his rich experience and current work on anticipated impacts of new technology with milestones every ten years through 2045. Radical developments in technology would challenge public policy in ways that are difficult to imagine, but current policymakers and the AI community need to try. Currently, AI systems are superior to human capabilities in calculating and game playing, and near human level performance for data-driven speech and image recognition and for driverless vehicles. By 2024, large advances are likely in video understanding, episodic memory, and reasoning.

The roles of future workers will involve increasing collaboration with AI systems in the government and public sector, particularly with autonomous systems but also in traditional areas of healthcare and education. Advances in human-technology collaboration also lead to  issues relevant to public policy, including privacy and algorithmic transparency. The increasing mix of AI with humans in ubiquitous public and private systems puts a new emphasis on education for understanding and anticipating challenges in communication and collaboration.

Workforce

Patterns for the future workforce in the age of autonomous systems and cognitive assistance are emerging. Please take a look at Andrew McAfee’s presentation at the recent Computing Research Summit. Also, see the latest McKinsey ReportJobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. Among other things, this quote from page 20 catches attention: “Automation represents both hope and challenge. The global economy needs the boost to productivity and growth that it will bring, especially at a time when aging populations are acting as a drag on GDP growth. Machines can take on work that is routine, dangerous, or dirty, and may allow us all to use our intrinsically human talents more fully. But to capture these benefits, societies will need to prepare for complex workforce transitions ahead. For policy makers, business leaders, and individual workers the world over, the task at hand is to prepare for a more automated future by emphasizing new skills, scaling up training, especially for midcareer workers, and ensuring robust economic growth.”

Education for the Future

An article in Education Week “The Future of Work Is Uncertain, Schools Should Worry Now” addresses the issue of automation and artificial intelligence disrupting the labor market and what K-12 educators and policymakers need to know. A recent report by the Bureau of Labor Statistics “STEM Occupations: Past, Present, And Future” is consistent with the idea that even in STEM professions workforce needs will be less at programming levels and more in ways to collaborate with cognitive assistance systems and in human-computer teams. Demands for STEM professionals will be for verifying, interpreting, and acting on machine outputs; designing and assembling larger systems with robotic and cognitive components; and dealing with ethics issues such as bias in systems and algorithmic transparency.

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