The New York Times recently ran a story on the effectiveness of :"Big Data" computer search firms in finding talented candidates for businesses seeking to hire. The companies tout their use of objective data as a means of filtering out irrelevant bias in personnel decisions. Vivienne Ming, the chief scientist at one such company, Gild, described how she developed a personnel appreciation for this kind of bias, confirmed by studies, after she underwent her transition to life as a woman:
As a woman, Dr. Ming started noticing that people treated her differently. There were small things that seemed innocuous, like men opening the door for her. There were also troubling things, like the fact that her students asked her fewer questions about math than they had when she was a man, or that she was invited to fewer social events — a baseball game, for instance — by male colleagues and business connections.
Bias often takes forms that people may not recognize. One study that Dr. Ming cites, by researchers at Yale, found that faculty members at research universities described female applicants for a manager position as significantly less competent than male applicants with identical qualifications. Another study, published by the National Bureau of Economic Research, found that people who sent in résumés with “black-sounding” names had a considerably harder time getting called back from employers than did people who sent in résumés showing equal qualifications but with “white-sounding” names.
Paradoxically, it is precisely the search for modern, objective, and dispassionate recruiting methods that underscores the tenacious persistence and ongoing power of longstanding biases and stereotypes. And while the quest for computer matching of ideal candidates to their perfect jobs may seem both quixotic and impersonal, there is something to be said for a genuine effort to hire based on objective indicators of merit.