Job.com, Match.com?
Today’s Upshot politics and policy column of the New York Times “Can an Algorithm Hire Better Than a Human?” focuses on a new wave of software start-ups that use big data to automate the job recruitment process. By avoiding the personal biases of human interviewers (and potentially introducing some new ones in the form of opaque algorithms) these tools, which help employers sift through mountains of job applications to identify the right applicants, have the potential to improve hiring:
[H]iring could become faster and less expensive, and their data could lead recruiters to more highly skilled people who are better matches for their companies. Another potential result: a more diverse workplace. The software relies on data to surface candidates from a wide variety of places and match their skills to the job requirements, free of human biases.
The article raises the question of whether a computer will do a better job of giving non-credentialed and diverse applicants access to the new jobs and giving employers access to job seekers with more relevant skills and know how than the average interview-based process:
One engineer had applied twice to Rackspace, a cloud computing company, without luck. As an Army veteran who worked in public radio with no high school degree or professional programming experience, he did not fit the pattern that Rackspace looked for. But Gild suggested him based on the software he had been writing on his own, and he was hired.
The upshot? The interview isn’t disappearing anytime soon and data-driven tools might help people whose resume otherwise would have ended up in the trash heap to score one. But although the technologies of expertise can help to match people to opportunities they might not have known about or had access to before, machine tools must be combined with human intelligence, empathy, and sensibility if we are to create fair and well-functioning workplaces. Matching people to opportunities to work, volunteer, and serve will depend squarely on both. At this early juncture in the use of such tools to make labor markets more effective, the key to success will be to ensure that processes — whether human or machine — are transparent and evolving.
