Recruitment Grapevine: - Rec firms use THIS tech to flag sexist and racist traits

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Rec firms use THIS tech to flag sexist and racist traits among candidates

Job interviews, on the whole, successfully give recruiters an insight to a candidate’s personality, skills set and experience. But there is always a high likelihood that interview responses are heavily rehearsed and unreflective of a candidate’s real behaviour.

This is why California-based firm Fama Technologies has rolled out a new way to screen candidates that seeks to identify risks associated with applicants prior to getting hired.

Cnn.com reported that the company uses Artificial Intelligence (AI) to trawl through a person’s public digital footprint and to highlight any behaviours that could be a cause of concern such as sexual harassment, prejudice and bullying.

A New York-based Employment Lawyer, Alex Granovsky told the publication: "If you make a hire and it turns out they were posting, sexist, racist and other lunacy online...that is not only a liability for an employer, it also calls into question your ability of making a hiring decision."

Fama reportedly creates risk profiles for job applicants by scouring public social media profiles.

While this AI tool may successfully highlight risky hires to prospective employers, it is not to say that this technology isn’t flawed.

Earlier this year, HR Grapevine reported on Amazon recruiters that were forced to pull the plug on an AI recruitment tool after it started to show a strong bias for male candidates.

Fama’s CEO, Ben Mones, commented: "We do not score; there's no thumbs up or down.

“We aren't saying anything about the person ... We can say this piece of text is an example of bigotry."

What happened with Amazon’s AI recruitment tool?

The team had been working on computer programmes with the aim of mechanizing the search for top talent since 2014, according to a report in Reuters.

The group focused on creating software that could recognise 50,000 terms that showed up on past candidates’ resumes, and therefore determine which particular phrases were linked to candidates that went on to be hired, and those that lead to a candidate ultimately being dismissed.