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7 Recruiting AI Terms Every Recruiter Needs to Know

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Interest in artificial intelligence (AI) recruiting technology has exploded recently. From finance to sales departments, business leaders are asking how they can leverage AI technology to become more efficient, cost-effective, and competitive. HR is no exception.

To stay on top of this trend, here are seven recruiting AI terms that every recruiter needs to know.

  1. Artificial intelligence

Artificial intelligence (AI) is a machine that can mimic human abilities such as learning, problem- solving, planning, perception, and the ability to move objects.

In a nutshell, AI requires large amounts of data as inputs to produce an output which is a solution to a problem. Core areas of AI include machine learning (e.g., Netflix recommendations), machine perception (e.g., Apple’s Siri), and robotics (e.g., self-driving cars).

How AI is used in recruiting

AI for recruiting is the application of artificial intelligence such as learning or problem-solving to the recruitment function. Recruiting AI technology is designed to automate some part of the recruiting workflow, especially repetitive, high-volume tasks.

Applications of recruiting AI technology that currently exist include automated resume screening, recruiter chatbots, and digitized interviews.

  1. Algorithm

An algorithm is a procedure or formula that takes inputs through a sequence of steps to produce an output in order to solve a problem.

How an algorithm is used in recruiting

The simplest form of an algorithm used in recruiting is a keyword or Boolean search. The problem here is identifying qualified candidates from a larger applicant pool, the inputs are your search terms, and the output is a shortlist of candidates who meet your search specifications.

An example of how an algorithm is used in recruiting AI technology is intelligent resume screening. The problem here is the same: identifying qualified candidates from a larger applicant pool.

Instead of using pre-selected search terms, this type of machine learning algorithm trains itself on prior employees to learn which resume data points (inputs) are correlated with successful employees to produce a shortlist of qualified candidates (output).

  1. Machine learning

Machine learning is a type of computer program or algorithm with the ability to teach itself by analyzing data (inputs) and coming up with a solution (output).

A machine learning algorithm continues to learn from new data you input to increase the accuracy of the solution it comes up with.

How machine learning is used in recruiting

Machine learning algorithms in recruiting AI technology is being used to automate resume screening to shortlist and grade candidates by learning from existing employees’ resumes.

Machine learning algorithms in recruiting software are also being used assess candidates’ personality, and job fit through digitized interviews by learning from successful candidates’ facial expressions and word choices.

  1. Natural language processing

Natural language processing is the ability of a computer program to understand human speech as it is spoken or written.

How natural language processing is used in recruiting

One way natural language processing is being used in recruitment automation technology is through AI chatbots that provide answers, feedback, and suggestions to candidates in real time.

Based on candidates’ replies and feedback, the chatbot uses machine learning to teach itself to become more accurate in its answers when interacting with other candidates in the future.

  1. People analytics

People analytics is the use of data and data analysis techniques to understand, improve, and optimize the people side of business.

People analytics links people data (inputs) with different types of business data using predictive algorithms to produce outcomes (outputs) aligned with company goals such as increased revenues and lowered costs.

How people analytics is used in recruiting

People analytics isn’t a recruiting AI term on its own, but it falls under the same umbrella of leveraging data and technology to optimize HR and recruiting processes.

  1. Predictive analytics

Predictive analytics is a catch-all term for the application of a statistical equation or algorithm to a data set (inputs) to create a predictive model (output) that determines a numerical value of a future probability.

In many cases, the data set used contains multiple variables that are believed to be predictive of a particular outcome.

How predictive analytics is used in recruiting

Predictive analytics can be applied to candidates to predict which ones are likely to be successful employees. This predictive model can be created using resume data, pre-hire assessments, or interview scores.

For a predictive model that uses resume data as its inputs, the variables could include education level, years of experience, skills, and personality traits.

Predictive analytics can also be applied to employees to predict which one are likely to quit. This predictive model may use multiple variables such as commute distance, company tenure, employee engagement, and compensation.

  1. Sentiment analysis

Sentiment analysis is the ability of a computer program to determine the subjective opinion, emotional state, or intended emotional effect of spoken or written word.

The basic form of sentiment analysis is classifying the polarity of a given text: positive, negative, or neutral. More advanced sentiment analysis classifies text into specific emotions such as “angry” and “happy.”

How sentiment analysis is used in recruiting

Sentiment analysis is being used to identify potentially biased language in job descriptions. The program is fed inputs that words such as “aggressive” are perceived as masculine-sounding whereas words such as “collaborative” are perceived as feminine-sounding.

By analyzing the words used in a  job posting, the program can create output in the form of suggested replacement words in order to help solve the problem that these words may be discouraging female candidates from applying.

The takeaways

The dominant theme in recruiting right now is AI for recruiting. It’s clear that tech-enabled recruiting is here to stay. Give yourself a leg up by familiarizing yourself with the AI recruiting technology terms below:

  1. Artificial intelligence
  2. Algorithm
  3. Machine learning
  4. Natural language processing
  5. Predictive analytics
  6. Sentiment analysis

A version of this post was first published on Ideal.

Photo Credit: jhuniorig Flickr via Compfight cc

An Algorithm Couldn't Have Hired Steve Jobs

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Here’s the plan: buy the newest, greatest HR software that’s part of the new wave of hot technology attracting lots of eager investment, make the big announcement, reassure all the hiring managers it’s an easy interface, and then what? Just see what happens when real people use it? Hmmm.

I’m thrilled that HR technology is rising to the fore, with innovative companies like Workable, recently infused to the tune of $34 million. But it is important that you understand how to connect that technology with the people with whom it will interface.

What Workable aims to do is give smaller firms the same bells and whistles, big-as-the-Cloud-allows hiring software that the big ones have. It’s a way to even the playing field in terms of the hiring process, and with customization make sure that it’s not that dreaded LCD, one-size-fits-all tech none of us want.

We may still be at the stage where we know more about what we don’t want than what we do.

I’ll give you a leg up with that one: we do want software that can run on mobile platforms and understands how to work with social media. Apropos of that, Workable’s mobile-friendly and social literate. There’s also the reality of the cost factor: hiring is expensive; technology is expensive; the global workplace makes managing talent expensive. If we could save a bit of cash, that’d be great. (And we will further discuss hiring needs, costs and processes in next week’s live podcast and #TChat with Nikos Moraitakis, CEO of Workable.)

But — and you know there’s a but or there would not be a column — for all the efficiency that technology creates, it can’t replace a real human handshake. It can’t replace that human element. And part of the mythology surrounding mega talents like Steve Jobs or Danielle Fong (the co-founder of energy storage innovator LightSail) are the many people involved in those particular, individual career paths. To be perfectly honest, I’m not sure an HR search application would be able to locate Steve Jobs now, though it would certainly locate the networking maven Fong.

Justin Sullivan/Getty Images

Hiring isn’t an algorithm. It’s still about people and talent and culture fit. Nor is it a grab for the file in the middle of the stack. So the ideal hiring manager needs to be able to straddle both sides of the equation. The hiring managers need to be facile at the new technology — which is designed specifically to reduce the amount of time doing all that busywork, from search and recruiting and on through the hiring funnel. But they also need to be able to do the face to face. This is a two-handed drive: you’ve got to speak both languages in order to keep up.

But there’s another reason to make sure human stays in HRand you are really good at the software. Given that your hiring team is the first face of your employer brand, you’ll want them to be fluent with your selected HR technology, whatever it is. If they’re not, it will have an enormously negative impact on your employer brand. Nothing is a more damaging sign to a company, particularly a small business, than a clumsy and inept hiring and onboarding process. It says: This is a company chasing numbers, not talent. So much for transparency: you’re just transmitting that your apparent transparency is actually see-through.

Inefficient as a mindset

Sure, Las Vegas was muy tasty: so much HR technology, so many new rollouts, that there’s bound to be pleas for caution after we’ve finally emerged from the buffet. As a field, however, we’ve seemed wedded to a kind of balky inefficiency.

There are the “approval up the channels” steps and pauses, and the “can’t find the references” glitches, and countless ghosts in the machine. Then there are the machines. We are haunted by the very process we’re trying to leave behind. Why? There’s still an interface with seams that need erasing. Now it’s from the amazing HR technology to the human hiring teams. Let’s make sure those key dots are connected. Go for it and please keep me posted.

A version of this post was first published on Forbes on 10/23/15