Why we should train workers like we train machine learning algorithms

The evolution of workforce opportunity in the United States depends on the future of education and our commitment to far-reaching, equitable federal reform. Unfortunately, policy conversations at the federal and state levels about transforming education systems to meet future workforce demands have focused disproportionately on a skills agenda, largely ignoring behavioral competencies that often complement and enhance the value of technical skills. This misguided approach equates 21st-century workforce development with skills acquisition, which only serves to reinforce a two-tiered workforce: those who are best positioned to acquire and monetize their skills will be granted mobility and long-term security while all others continue to be stranded on the bottom rung of the socioeconomic ladder. Developing intelligent policies to combat workforce inequality requires acknowledging that employer demand for "skills" actually refers to a constellation of content knowledge, technical abilities, and applied intelligence. Per the National Association of Colleges and Employers' 2018 Job Outlook survey, eight out of 10 employers reported that applicants' problem-solving and teamwork abilities influenced hiring decisions; only six out of 10 employers reported the same for technical skills.