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 input 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.
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).
3. 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.
4. 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.
5. 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.
6. 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.
7. 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 input 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 suggest alternative words to help solve problems such as use of words that may discourage female candidates from applying.
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:
- Artificial intelligence
- Machine learning
- Natural language processing
- Predictive analytics
- Sentiment analysis
For more on this topic, see Can Technology Replace Human Interpreters?Comments