“Algorithms are fair. They are reliable. They learn from their mistakes and can tell you what it was about top performing candidates that made them top – so the algorithms can find more. Algorithms give the same amount of time and energy to each candidate. They are unbiased. They don’t get tired after screening three thousand (or three million candidates).”
– Greta Roberts, CEO & Co-founder of Talent Analytics
Are they coming for us? Are they going to take our jobs? Why are we so afraid that leaps in technology are going to make us obsolete? In reality, they’re likely to make our lives infinitely more productive, and interesting.
It’s good to remember that despite claims that robots are coming for our jobs, only 5% of all occupations are at risk of being entirely automated.
However, according to a McKinsey Global Institute report, jobs will change dramatically, forcing workers to adapt.
McKinsey’s analysis of 800 occupations and 2,000 job tasks predicts that half of workers’ current tasks could be automated by the year 2055 — using technology that currently exists.
Summer Husband is Senior Director of Data Science for Randstad Sourceright, where she’s responsible for “helping to bring recruiting data to life.”
Her superpower is taking advanced mathematical algorithms and finding real world applications for them. She says technology can assist us in making leaps forward in both productivity and prediction — and that machine learning can be harnessed to make our lives and work more efficient.
“Let machines do what machines do well: coming up with connections that may not be obvious to tease out as a human,” she said.
“But then let humans do what they do well: building relationships, giving context, not married to the output of an algorithm.”
SEND IN THE ALGORITHMS
So what does this mean for recruiting? Husband says it needs to start with good communication, to bridge the divide between the data and the practices.
“As a data scientist, it’s really important for me to talk to the people who really understand the underlying problems,” she said.
“So I need to talk to recruiters and understand, when you’re looking at a resume, as a human, what is it that you notice that means this person is a good fit, what are the red flags that tell you, no, this doesn’t work? And then I would try and work out, how do you quantify that?”
THE CENTAUR APPROACH
No, it’s not a mythical half man, half horse. But it is the term being used to describe the half-human, half-technological approach being adopted as more automation creeps into our recruitment practices.
Husband tells a story about two chess amateurs with computers, who, in 2005, beat both a human grand-master chess champion and a pure Artificial Intelligence called Hydra.
“These guys were not experts at chess, and they did not have the state of the art AI,” she said.
“The reason they won this was because they were experts at harnessing a computer…They understood how to take advantage of a computer to win it.”
It’s a powerful example of machine learning that we can use in recruiting. What are the areas that it really makes sense to let a machine handle? What makes more sense for a human to handle?
BRING ON THE ROBOT OVERLORDS
Recruitment guru Glen Cathey from Randstad Holding has been talking about the implications of AI on recruitment for almost a decade.
“I welcome the computer overlords,” he laughs. “I’m not nervous about computers taking my job. Unless you’re not adding value to the process, in which case you should be scared.”
Cathey cites examples of lawyers utilizing AI tools to lighten their search loads – and says there is no reason recruiters couldn’t do the same.
“There’s just so much more data now that you need these technologies to boil the ocean for you”
– Jay Leib, lawyer who created AI technologies to assist in legal research.
With AI, you can focus more time on engaging people rather than finding people, says Cathey.
In fact, he says, we would be wise to flip the AI to IA, or “Intelligence Automation”, quoting ethics and emerging technologies and research fellow at the Institute for the Future, Jamais Cascio:
“Intelligence augmentation decreases the need for specialization and increases participatory complexity.”
Put simply, using technology means we can do more, we can do it better, and we can do it faster.
According to the McKinsey report, productivity looks likely to increase, despite all of these simple tasks being taken away. Globally, it could rise by 0.8% to 1.4% annually over the next 50 years.
So rather than worrying robots are going to take our jobs, let’s work out how we’re going to keep them busy. We’re going to need their help.