7 Recruiting AI Technology Terms You Need to Know

Interest in recruiting AI technology has exploded recently.

recruiting ai technology

From finance to sales departments, business leaders are asking how they can leverage technology to become more efficient, cost-effective, and competitive. HR is no exception.

To stay on top of this trend, here are 7 recruiting AI technology 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 solves 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 technology

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 intelligent resume screening, recruiter chatbots, and digitized interviews.

2. Algorithm

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

How an algorithm is used in recruiting AI technology

The simplest form of an algorithm used in recruiting is a keyword or Boolean search. The problem you’re trying to solve 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 criteria.

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).

Related terms: machine learning

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 AI technology

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

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

Related terms: algorithm

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 AI technology

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.  

Related terms: sentiment analysis

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 AI technology

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

Related terms: HR analytics, talent analytics, predictive analytics

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 AI technology

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 multiple 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 AI technology

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.

Related terms: natural language processing

The takeaways

The dominant theme in recruiting these days is AI for recruiting. It’s clear that AI tech-enabled recruiting is here to stay in areas such as resume screening, chatbots, and interviews.

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. People analytics
  6. Predictive analytics
  7. Sentiment analysis

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Ji-A Min

Ji-A Min

Head Data Scientist at Ideal
Ji-A Min is the Head Data Scientist at Ideal. With a Master’s in Industrial-Organizational Psychology, Ji-A promotes best practices in data-based recruitment. She writes about research and trends in talent acquisition, recruitment tech, and people analytics.
Ji-A Min


  • itban Omair

    Dear Min,
    thanks for this wonderful blog. I am newbie in Machine learning horizon. currently we have implemented new ATS tool Taleo in our organization. How can I implement Machine learning and predictive analysis on Taleo to provide some insight to my organization? can you help me with any generic business case if possible.

    • Glad you found the blog helpful 🙂 One of the best applications of machine learning for an ATS like Taleo is screening, shortlisting, and ranking resumes for an open role. Machine learning can handle this task more efficiently and objectively than humans are able to.

      If you provide your contact info, I’d be happy to provide more information. You can email me at jia@ideal.com.