LinkedIn’s job-matching AI was biased. The company’s solution? More AI.

ZipRecruiter, CareerBuilder, LinkedIn—most of the world’s biggest job search sites use AI to match people with job openings. But the algorithms don’t always play fair.

excerpt: 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.

“You might be recommending, for example, more senior jobs to one group of people than another, even if they’re qualified at the same level,” Jersin says. “Those people might not get exposed to the same opportunities. And that’s really the impact that we’re talking about here.”

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.

To address such issues, Jersin and his team at LinkedIn built a new Ai designed to produce more representative results and deployed it in 2018. It was essentially a separate algorithm designed to counteract recommendations skewed toward a particular group. The new AI ensures that before referring the matches curated by the original engine, the recommendation system includes a representative distribution of users across gender. 

Kan says Monster, which lists 5 to 6 million jobs at any given time, also incorporates behavioral data into its recommendations but doesn’t correct for bias in the same way that LinkedIn does. Instead, the marketing team focuses on getting users from diverse backgrounds signed up for the service, and the company then relies on employers to report back and tell Monster whether or not it passed on a representative set of candidates. . . . full story here