While fear can drive a desire among some policymakers to limit artificial intelligence (AI), the hope and promise of this advanced technology — including to create new jobs — cannot be overlooked. To capitalize on that potential, education must be a central part of the policy debate to ensure that more people are equipped with the skills they need to use and collaborate in an AI-enabled world.
Given the extent of AI’s disruption across business, transportation, health, safety, education, and more, there is an increased desire to govern it. For example, at the MIT AI Policy Conference earlier this year, government, business, and research leaders flagged social and ethical issues related to AI that they believe should be addressed at the policy level. The latest development in the AI debate is in New York State, where Gov. Andrew Cuomo recently signed legislation creating a commission to take on AI-related issues. As Cuomo stated, “Artificial intelligence and automation are already having a profound impact across many industries and their influence keeps growing, so it's critical that we do everything in our power to understand their capabilities and potential pitfalls.”
Even within the technology industry, itself, there are calls for greater commitment to developing safe, secure, and socially beneficial AI systems; this is the mission promoted by OpenAI, an AI development company in a $1 billion partnership with Microsoft. Ensuring that AI upholds high standards is desirable, but imposing limits on the technology is impossible. The very nature of computer science is changing as AI becomes ubiquitous in various degrees and applications, from robotics to software and in industries from healthcare to financial services.
Given this reality, policymakers need to bring education into the discussion, particularly to ensure that workers at all levels are “upskilled.” This is the best way to capture AI’s benefits, while also limiting the social and economic downside. As McKinsey stated, “As it turns out, companies using AI to also drive innovation are more likely to increase headcount than reduce it.” The jobs in the Fourth Industrial Revolution, however, require new knowledge and higher skills.
UN Secretary General António Guterres, while warning of the disruption of labor markets due to technological advances, has called for “retraining...at previously unimaginable scales. Education must adapt, from the earliest grades.” Reskilling needs to be the new norm across the business landscape to address the growing skills shortage and to position people for lifelong employment. As IBM CEO Ginni Rometty commented at Davos earlier this year, the global workforce needs to be equipped with next-generation skills to ensure the benefits of AI and other advanced technology are experienced widely, and not just by a few. That means both retraining for today’s workers and equipping students who will enter the workforce in the future with the knowledge and skills they need to compete.
Many employers are already embracing reskilling, among them Amazon, which recently announced a $700 million “Upskilling 2025” program for one-third of its workforce. The 100,000 workers to be retrained will be prepared for positions that they otherwise would be ineligible for, even if those new jobs are outside the company.
The question for policymakers, business leaders, and educators then becomes how best to deliver the most effective training. It is a significant challenge to meet the needs of workers who have varied backgrounds, experience, and levels of technical skills – many of whom have with a less-than-perfect academic record. Part of the answer is deploying advanced technology to solve this problem. Technology-based adaptive learning takes a personalized approach. By focusing only on building knowledge and skills where needed, competency can often be reached in half the time compared to traditional learning.
But even more important, adaptive learning can level the playing field for those who do need extra time and targeted support to reach competency. In traditional learning, time spent on learning is fixed, while the distribution among learners is in performance. Some will pass and some will fail – either directly or more passively by getting a “stamp” for attendance, while not learning very much. Adaptive learning takes the opposite view: Performance is fixed, with the assumption that everyone achieves proficiency, and time is the variable. An intelligent adaptive learning system dynamically adjusts what an individual needs to get to proficiency, but the time to get there may vary as much as an order of magnitude. If the goal is to equip as many people as possible with the knowledge and skills needed in the 21st century workplace, then ultimately reaching proficiency is more important than the time it takes to get there.
Reskilling as many workers as possible and at all levels creates and supports greater equality of opportunity. When more people with higher skill levels have improved prospects for lifelong employment, fear is minimized. Then the hope and promise of the AI-enabled workplace can be realized — tangibly and realistically — in areas where jobs are not disappearing but being created.