A Bright Side to Big Data

One of many good questions that came up at the Alternatives to Compulsory Education conference last Saturday was raised by a person who works in human resources. She noted that while she goes out of her way to seek and interview applicants with nonacademic backgrounds—and those she hired have performed well—most people in her field do not seek such applicants. Because human resource personnel have such a large pool to select from, they can ignore those resumes that are unconventional and focus on the ones that have conventional signals of success, such as graduation from college. As this person noted, those conventional signals are not particularly accurate for determining who will be a good employee, but they are customary and widely used by business, so how do we help homeschooled teenagers who don’t have college degrees find work? Must they go to college?

I responded that, first, only 27% of Americans, as of 2011, hold four-year college degrees, so the idea that we all need them in order to have the economy run and create innovation is crazy. The argument that college graduation rates are driving our economic and technological advances is a half-truth at best; our nation’s incredible growth after World War Two occurred with less than a quarter of the country having college degrees, and many important technological and commercial advances in recent decades have occurred outside the university by college dropouts. It should also be noted there are a lot more college graduates working as baristas and retail sales clerks now, too. Just graduating college doesn’t guarantee you more money and a better job than someone who doesn’t graduate. Second, there is more to be being an effective employee than owning a four-year college degree, and the cost of college is making course completion certificates and other less expensive signals attractive options to both employers and prospective employees. While the push to get everyone to get college degrees has resulted in a record-high level of Americans doing so, The Washington Post reports that “. . . in terms of future earnings, education level matters less these days than in previous generations, and field of study matters more.”

In my answer I also mentioned books like DIY U and Hacking Your Education and the value of learning computer coding on your own in order to find work today, as well as the many opportunities unschooling provides for creating a portfolio of travel, work, apprenticeships, and internships that prove to employers not only your self-sufficiency and resourcefulness, but what you actually can do.

However, the fear that your adult child won’t be considered for a particular job because a check box was not filled on their high school transcript is real for many people. The independent ways of proving you are qualified for hire that I described may seem more burdensome to some than merely presenting evidence of college graduation to an employer, so I know we need more solutions than what I proposed.

Then, on Monday after the conference, I read about Gild, a company that provides recruits to technology companies by using their proprietary software to sift through data on the Internet and locate promising developers. The New York Times reports that the new field of “work-force science” searches for talent using different signals for success: “How well does the person perform? What can the person do? And can it be quantified?” This is, to me, a hopeful path for Big Data to take: people will be judged based on the merits of what they can actually do, rather than just use a college degree as a substitute for competence. In the Times article, Dr. Vivienne Ming, Gild’s chief scientist is described: “She doesn’t think Silicon Valley is as merit-based as people imagine. She thinks that talented people are ignored, misjudged or fall through the cracks all the time. She holds that belief because she has had some experience of it.”

Gild is focused on providing programmers to Silicon Valley by searching the Internet for candidates using their algorithms:

“Is his or her code well-regarded by other programmers? Does it get reused? How does the programmer communicate ideas? How does he or she relate on social media sites? . . .

“Everybody can pretty much agree that gender, or how people look, or the sound of a last name, shouldn’t influence hiring decisions. But Dr. Ming takes the idea of meritocracy further. She suggests that shortcuts accepted as a good proxy for talent — like where you went to school or previously worked — can also shortchange talented people and, ultimately, employers. “The traditional markers people use for hiring can be wrong, profoundly wrong,” she said.

Dr. Ming’s answer to what she calls “so much wasted talent” is to build machines that try to eliminate human bias. It’s not that traditional pedigrees should be ignored, just balanced with what she considers more sophisticated measures. In all, Gild’s algorithm crunches thousands of bits of information in calculating around 300 larger variables about an individual: the sites where a person hangs out; the types of language, positive or negative, that he or she uses to describe technology of various kinds; self-reported skills on LinkedIn; the projects a person has worked on, and for how long; and, yes, where he or she went to school, in what major, and how that school was ranked that year by U.S. News & World Report.

“Let’s put everything in and let the data speak for itself,” Dr. Ming said of the algorithms she is now building for Gild.

There are other companies described in the article who are also doing what Gild does, so it is likely this area will grow beyond just the search for talented programmers. The article notes that Dr. Ming “wants to expand the algorithm so it can search for and assess other kinds of workers, like Web site designers, financial analysts and even sales people at, say, retail outlets.”

Using Big Data to create a true meritocracy is an interesting concept that holds more promise for unearthing talent than our current system, where socio-economic status often trumps merit. Homeschoolers and unschoolers can definitely benefit from this innovation, as can employers. I wonder: is there any support or interest among hackademics to explore this technology and ramp it up for use by those who can’t, or don’t want, to attend college but who nonetheless have marketable skills and knowledge?