"It's not surprising that algorithms are becoming very attractive to eradicate the risk of bias and take the decision out of the hands of an interviewer," says Emma O'Leary, a consultant with Manchester-based employment law firm Elas.
Don't imagine being grilled by R2D2 at a desk, the reality is different. The most common iterations are automation tools, typically deployed to filter out unconscious bias in the early stages of the hiring process.
As well as helping to distinguish people, AI can profitably target those perhaps normally deterred from applying. For example, seeing a lack of women responding to its data-scientist vacancies, cyber-security company Panaseer turned to the predictive algorithms of Textio. This uses machine learning to identify gender-biased language in job descriptions and suggest linguistic tweaks.
"It highlighted how some of the wording in our job posts such as 'ambitious', 'tackle' and 'driven' was typically associated with masculine traits which was actually creating a subconscious bias," said chief scientist Mike MacIntyre.
"Alternatives were recommended to make the descriptions more inclusive and appealing to women."
The simple amendment led to a 60 per cent rise in female candidates and an all-female shortlist for one of its most recent positions.
Using AI to provide the legwork before reverting to the human touch in the final stages remains a default approach for those who still feel there is a role for emotional intelligence in the hiring process. While almost two thirds of respondents surveyed by software developer Pegasystems expected the use of AI to conduct interviews and shortlist candidates to be standard practice in the next decade, only 30 per cent felt an algorithm would be making the final hiring decisions.
Cognisess has developed AI-powered software that is designed to emulate an interview. The machine learning assesses candidates across a number of performance areas, while the video element films them responding to a set of questions which are then reviewed by a robot primed to analyse the minutiae of facial expressions frame by frame.
If a company requires passion and enthusiasm for a customer-facing sales role, it will home in on the level of positivity and expressiveness of the person. A fake smile will not be enough to cut it.
"The machine can detect micro expressions," explains Boris Altemeyer, the firm's chief scientific officer.
"These emotions show on the face for only a fraction of a second."
One client, Intercontinental Hotels Group, has increased the diversity in its hiring and saved £250,000 in the assessment process.
"Technically, this system can recruit in its entirety, but we would never advocate removing people completely from the process. If you think about humans reviewing 60 or more video interviews a day and still being absolutely unbiased or as sharp as when they watched the first one, that would be a tall order for anyone, so it's about getting as much of the purist data to them, so they make the best decisions."
For the time being at least, the human recruiter is still hired.
