Canadian HR Reporter

CANADIAN HR REPORTER DECEMBER 2019 10 NEWS sentiment analysis, keywords anal- ysis, depending on how you use that, can be good or bad," he says. "We're in the very early embryonic stages, whether you believe it or not, of how artificial intelligence can help us during the recruiting process. And I feel like this is a learning lesson." Unilever is a smart company that knows the traits it's looking for, and it would have analyzed video data from previous success- ful candidates, looking for trends and patterns within that, he says. "I'm sure [HireVue has] used what we call an equal data set, which means there will be a good blend of males, females, racial backgrounds, class backgrounds, college backgrounds, all of those various different things in order to give people a fair shot." Plus, HireVue is not offering sentiment analysis — such as as- sessing whether a person is being dishonest — because that poten- tially could be a misuse of AI, says Stewart. While Unilever knows this is not the final answer in the recruit- ment process, the AI software has saved hours' worth of screening time and millions of dollars, and obviously the company has made successful hires, he says. "It's not perfect. But then nei- ther is the hiring manager that's interviewing perfect because you get personality clashes during the hiring process, you can get two completely conflicting people from different classes, races, backgrounds, whatever, who both meet over an interview, and they just don't necessarily click... Nobody talks about that stuff. But that's just as much of a chal- lenge as any artificial intelligence system is." And, obviously, this process is more efficient, says Stewart. "It used to take statistically a good recruiter six seconds to read a resumé. And then artificial in- telligence came along and meant that you could analyze and match 600 resumés per three million jobs per second, which meant you could do it on a huge scale." e next stage to the technology is the interview process, he says. "Instead of having to hire and filter through… 10 hires per hir- ing manager, now a hiring man- ager can do 30 or 40. And that's the difference; it's efficiencies." is kind of approach will also appeal to the younger generation, says Stewart. "We're used to tech, we live our lives through social; the whole world knows what we do from Facebook, Instagram, Snapchat, you name it," he says. "is is very much built for our generation be- cause, by 2025, 70 per cent of the labour force will be millennial. So, we are the dominant labour force." 'Early days' for AI and recruitment When it comes to recruitment and AI and algorithms, "it's ex- tremely early days," says Jeff Aplin, president and CEO of the David Aplin Group in Calgary. "I wouldn't even consider it early-adopter phase, although the fact that some large employ- ers are using it may be contrary to that comment." ere will be a very slow adop- tion process when it comes to trusting robots in hiring people, and assessing people's fit with a team, an organization's core val- ues and culture, he says. "I'm skeptical for sure… there's no question that some of the re- petitive tasks in recruitment se- lection and talent management are… being replaced through ar- tificial intelligence. But in terms of the actual hiring decision, I think it's going to be quite a long time before we see hiring managers and employers in Canada trust- ing those kinds of systems." Having offered video interviews through his work, Aplin says hir- ing managers still like to meet up with candidates in person or have a live chat on Skype. "When humans sit down with another human and have a con- versation with them, they gen- erally are more open-minded through that process to go 'OK, 'm seeing something here that maybe I wouldn't have seen in my initial three- to five-second response to seeing them on screen.'" But when it comes to an em- ployer hiring for a large event over a short period, the AI approach could make sense, say Aplin. "You've got a lot of process upfront in the weeks leading up to that to screen people and hire them and onboard them and train them and orient them and all that stuff. So… technology can help accelerate those larger-volume hires where you're dealing with a repetitive task." And there may be specific job categories where this could be effective, such as the hospitality industry, where it's about "being particularly friendly and outgoing and approaching people at a table and engaging in conversation… and then selling to them that way," he says. "That might be some low- hanging fruit, for example. And so that kind of job category might be one that gets a bit more trac- tion sooner." But when it comes to hiring for a more analytical or creative or leadership role, "I really do struggle with how they're going to use the robots for that in the near future," says Aplin. "Creative pur- suits, problem-solving, analytical functions, I think those will be very difficult, at least in the short term, to replace by a simple video and asking robots to analyze their facial tics and movements and smile, ratios and stuff like that." And what if a candidate has a facial deformity, asks Aplin. "How is that going to work be- cause they might be fantastic at a role, but how adaptable and mal- leable will that AI algorithm be?" he says. "Humans see the bigger picture; robots don't generally see the big- ger picture. And so, if you're deal- ing with people with disabilities or who have facial deformities, a speech impediment, these are all people with great ability and great talent to hire into specific job cat- egories. Do you want to risk not considering them fully because you've outsourced your decision- making to a robot? I wouldn't." 'Not yet ready for prime time' Jonathan Gratch, director for virtual humans research at the Institute for Creative Technolo- gies in Playa Vista, Calif. agrees the technology is "not ready for prime time," citing its limitations. While many companies might think it's a cool approach that will attract interest, they prob- ably haven't thought it through very carefully, he says. ere's also the concern about algorithmic bias, where these technologies aren't trained uni- formly on different races or eth- nicities, says Gratch. "You have to know what that algorithm has actually learned and be able to expect it. And in many cases, machine-learning al- gorithms are just a black box, you actually have no clue… My pre- sumption would be the data they train this algorithm on is a human decided that this is a good or bad candidate. And so… if the people that made that decision are bi- ased, then the algorithm is learn- ing the same biases people have, and it's not clear that it's actually addressing that problem." And while the software may be able to capture facial expressions reasonably well in a controlled laboratory environment with good lighting, with the person looking straight at the camera, in a less controlled setting, it might not be as effective, says Gratch. "When you light from above or below with harsh light and create shadows, that might look to the machine eye that someone has a certain facial expression which they're not holding," he says. "If you're wearing dark glasses, if your hair is over your eyebrows, if you have dark skin or… lots of wrinkles, they can make incorrect inferences. [And] even if you can infer what the facial expression is, taking from that to infer some- thing about 'Is this a good candi- date?' is somewhat problematic." 'How malleable will that AI algorithm be?' AI RECRUITMENT < pg. 1 Unilever is using AI software with facial scanning technology to screen job candidates in the United Kingdom. Credit: CentralITAlliance (istock)

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