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Economic Justice Will Depend on “People Analytics”

Big data is reshaping access to jobs — especially low-skilled and semi-skilled service jobs — across the U.S., and this month’s Atlantic cover story offers the best-yet analysis of the trend.

Before the 1964 Civil Rights Act, employment tests were popular for jobs at all levels, from store clerks to future executives. In the ’70s and ’80s such tests fell out of use: Anti-discrimination laws “made HR departments wary of any broadly applied and clearly scored test that might later be shown to be systematically biased. Instead, companies came to favor the more informal qualitative hiring practices that are still largely in place today.” These informal methods are also prone to many kinds of bias, and can be harder to police than systematic tests.

Today, new technologies have brought automated systems back to the forefront of hiring and promotion. This is especially true for hourly service jobs, where requirements are consistent but turnover is high. Xerox, for example, uses big data to color-code job applicants who want one of the 45,000 jobs in its 150 U.S. call centers, using software from a startup called Evolv.

Xerox [has] switched to an online evaluation that incorporates personality testing, cognitive-skill assessment, and multiple-choice questions about how the applicant would handle specific scenarios that he or she might encounter on the job. An algorithm behind the evaluation analyzes the responses, along with factual information gleaned from the candidate’s application, and spits out a color-coded rating: red (poor candidate), yellow (middling), or green (hire away).

Evolv’s system considers a huge quantity of data — not just the applicant’s answers on the test, but also things like which web browser he or she is using to take the test. When making its assessment, the company deliberately ignores some information in order to avoid discriminatory effects. (It’s not clear to what extent other providers of similar services do the same.) As the Atlantic piece explains,

The distance an employee lives from work, for instance, is never factored into the score given each applicant, although it is reported to some clients. That’s because different neighborhoods and towns can have different racial profiles, which means that scoring distance from work could violate equal-employment-opportunity standards.

Despite these risks, big data in hiring may yet turn out to be good news for traditionally disadvantaged job applicants: The Atlantic reported that “nearly all” of the people interviewed for its story find the data “lead[s] them toward pools of candidates who didn’t attend college—for tech jobs, for high-end sales positions, for some managerial roles. In some limited cases, this is because their analytics revealed no benefit whatsoever to hiring people with college degrees; in other cases, and more often, it’s because they revealed signals that function far better than college history, and that allow companies to confidently hire workers with pedigrees not typically considered impressive or even desirable.”

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