Gender Biased AI in Job Searches Now a Thing

June 30, 2021

From initial search to applications to interviews, job hunters are now steered through the process by algorithms. Employers’ demand for AI solutions has surged with the pandemic, but there is a problem—the approach tends to disadvantage women applicants. An article at MIT Technology Review describes one website’s true bro response: “LinkedIn’s Job-Matching AI Was Biased. The Company’s Solution? More AI.” Reporters Sheridan Wall and Hilke Schellmann also cover the responses of competing job search sites Monster, CareerBuilder, and ZipRecruiter. Citing former LinkedIn VP John Jerson, they write:

“These systems base their recommendations on three categories of data: information the user provides directly to the platform; data assigned to the user based on others with similar skill sets, experiences, and interests; and behavioral data, like how often a user responds to messages or interacts with job postings. In LinkedIn’s case, these algorithms exclude a person’s name, age, gender, and race, because including these characteristics can contribute to bias in automated processes. But Jersin’s team found that even so, the service’s algorithms could still detect behavioral patterns exhibited by groups with particular gender identities. For example, while men are more likely to apply for jobs that require work experience beyond their qualifications, women tend to only go for jobs in which their qualifications match the position’s requirements. The algorithm interprets this variation in behavior and adjusts its recommendations in a way that inadvertently disadvantages women. … Men also include more skills on their résumés at a lower degree of proficiency than women, and they often engage more aggressively with recruiters on the platform.”

Rather than, say, inject human judgment into the process, LinkedIn added new AI in 2018 designed to correct for the first algorithm’s bias. Other companies side-step the AI issue. CareerBuilder addresses bias by teaching employers how to eliminate it from their job postings, while Monster relies on attracting users from diverse backgrounds. ZipRecruiter’s CEO says that site classifies job hunters using 64 types of information, including geographical data but not identifying pieces like names. He refused to share more details, but is confident his team’s method is as bias-free as can be. Perhaps—but the claims of any of these sites are difficult or impossible to verify.

Cynthia Murrell, June 30, 2021


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