Science Education is Lagging, Business Groups Say
Not enough American students are studying science, engineering and mathematics, a consortium of business organizations warned this week, posing a threat to the nation’s economic vitality and security.
“U.S. scientific and technological superiority is beginning to atrophy even as other nations are developing their own human capital,” they said.
Among their recommendations the business executives called for increased funding in basic research, reform of immigration policies to attract and retain foreign students, and improvements in public education in the sciences leading to a doubling of bachelor’s degrees in science, technology, engineering and mathematics by 2015.
They also noted the need for an expedited security clearance process. “Delays in processing security clearances continue to discourage U.S. citizens from filling vital technical positions that require clearances,” they wrote.
See “Tapping America’s Potential,” July 15.
Ideally, scientific education would do more than produce qualified industrial workers. To the extent that it encourages critical thinking and reality testing, scientific training can also promote and strengthen democratic values.
Americans are paying too much for almost everything, because the United States has long treated its trucking industry as an artifact to be preserved rather than as an opportunity for innovation.
These ideas aim to advance the detailed policy solutions needed to foster public trust and implement fairness in the adoption of AI across diverse domains, from healthcare and government benefits to rural access, education, and worker protections.
The evidence is clear: algorithmic pay-setting is established in app-based work, and payroll/timekeeping failures show how software can produce systemic wage harm at scale
While a few states have taken steps to implement decision-making mechanisms for certain AI systems, too many leaders are simply accepting narratives about AI’s purported public benefit at face value – jumping to the “how” of AI implementation before thoroughly vetting potential systems and deciding whether they are appropriate to use at all.