According to President Obama’s Council of Economic Advisers (CEA), approximately 3.1 million jobs will be rendered obsolete or permanently altered as a consequence of artificial intelligence technologies. Artificial intelligence (AI) will, for the foreseeable future, have a significant disruptive impact on jobs. That said, this disruption can create new opportunities if policymakers choose to harness them—including some with the potential to help address long-standing social inequities. Investing in quality training programs that deliver premium skills, such as computational analysis and cognitive thinking, provides a real opportunity to leverage AI’s disruptive power.
AI’s disruption presents a clear challenge: competition to traditional skilled workers arising from the cross-relevance of data scientists and code engineers, who can adapt quickly to new contexts. Data analytics has become an indispensable feature of successful companies across all industries. This reality dictates that companies invest heavily in data analytics to remain competitive and profitable. Consequently, unlikely industries such as retail, banking, finance, and even agricultural firms are aggressively competing for talent with specific computational data science and programming skills. A recent IBM report expertly quantifies the scope and breadth of employers’ hiring demands, noting that “[d]emand for data-driven decision makers, such as data-enabled marketing managers, will comprise one-third of the data savvy professional job market, with a projected increase of 110,000 positions by 2020.” Herein lies a window of opportunity: the rapidly growing technical skills gap.
Investing in high-quality education and training programs is one way that policymakers proactively attempt to address the workforce challenges presented by artificial intelligence. It is essential that we make affirmative, inclusive choices to ensure that marginalized communities participate equitably in these opportunities.
Policymakers should prioritize understanding the demographics of those most likely to lose jobs in the short-run. As opposed to obsessively assembling case studies, we need to proactively identify policy entrepreneurs who can conceive of training policies that equip workers with technical skills of “long-game” relevance. As IBM points out, “[d]ata democratization impacts every career path, so academia must strive to make data literacy an option, if not a requirement, for every student in any field of study.”
Machines are an equal opportunity displacer, blind to color and socioeconomic status.
Machines are an equal opportunity displacer, blind to color and socioeconomic status. Effective policy responses require collaborative data collection and coordination among key stakeholders—policymakers, employers, and educational institutions—to identify at-risk worker groups and to inform workforce development strategies. Machine substitution is purely an efficiency game in which workers overwhelmingly lose. Nevertheless, we can blunt these effects by identifying critical leverage points.
Investing in innovative education and training is an excellent place to start. Bill Gates’ recent $1.7 billion investment in U.S. public schools is a sign of the way forward, which offers two compelling messages for policymakers. First, innovate and experiment until we identify the right policies. Second, prioritize high-needs schools in poor neighborhoods; they deserve distinct attention to close their opportunity gaps and prepare them to be competitive in the future workforce.
Policymakers can choose to harness AI’s disruptive power to address workforce challenges and redesign fair access to opportunity simultaneously. We should train our collective energies on identifying practical policies that update our current agrarian-based education model, which unfairly disadvantages children from economically segregated neighborhoods. Evidence from a Harvard and New York University research study suggests attending a high-quality high school increases a student’s chances of attending a four-year college; which by extension improves their future income earning potential.
Let me ask a bold question: how much do we lose if we experiment with substituting an entry-level data science class for machine shop or a vocational carpentry program in urban high schools and community colleges? A 2010 pilot partnership between the University of California, Los Angeles and the National Science Foundation is an encouraging sign; the pilot focuses on redesigning computer science curricula in urban high schools to include newer mobile technologies and computational analysis.
Data science is an applied computational technology best suited to inquisitive minds, making it appropriate for young students. Google’s TensorFlow is an open source machine-learning platform; its free price tag makes the platform an accessible and scalable training resource for schools with constrained budgets. Introducing a data science program into urban schools would be a major paradigm shift for these students. An applied data science program teaching gateway coding skills such as Python, R, SQL, and computational analysis would boost employment possibilities and create meaningful pathways to economic mobility.
I am suggesting that we leverage AI’s transformative power to disrupt diminishing possibilities for marginalized groups, like young men of color, who often do not feature in innovative-themed discussions outside of the social justice arena. Open Source groups such as Code.org and StudentRND exemplify the kinds of transformational approaches that democratize access and opportunity.
Producing a diverse pipeline of tech-savvy workers for Google and Amazon, even if only at the entry level, is a more attainable dream for most cities than competing in a stacked race for Amazon’s HQ2. Broadening adoption of artificial intelligence technologies poses significant workforce challenges, but it also offers the chance to blunt these effects and create opportunities for marginalized groups if we act preemptively.
Google is a donor to the Brookings Institution. The findings, interpretations, and conclusions posted in this piece are solely those of the authors and not influenced by any donation.