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What Is AI Resume Screening? A Guide for Employers

AI resume screening can help your business get from posting a job to hiring the right person promptly and efficiently. Understanding the benefits and drawbacks of resume screening using machine learning, and knowing some best practices for adopting an AI system, can help you make the most of the latest technologies.

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What is AI resume screening?

AI resume screening is the process of using machine learning to identify high-quality candidates to move forward through the recruitment process. Unlike traditional applicant tracking software (ATS) that often reviews resumes using a fixed set of criteria, an AI system learns based on feedback you give it and improves its algorithm in response. As a result, the system can become more precise and efficient over time.

Types of AI for resume screening

There are three main types of AI for resume screening:

  • Keyword-based: This type of AI looks for specific phrases and patterns of text, prioritizing candidates’ resumes that feature the right words.

  • Grammar-based: This AI screening tool can break down phrases and sentences to gain an understanding of what a resume says and then use this information to identify quality candidates.

  • Statistical: Instead of using text, statistical AI systems utilize numerical data, such as timelines or the density of certain words on a resume, to select quality candidates.

Pros of resume screening using machine learning

Using AI resume screening tools offers a few key benefits for employers, including the following.

Improved staffing levels

AI resume screening tools can compile a short list of quality candidates in a fraction of the time it takes a human to produce one. As a result, AI can expedite the hiring process, allowing you to start interviewing for open positions much more quickly. In turn, positions will remain vacant for less time, reducing strain on your existing team and improving morale. 

Better candidate experience

With AI screening resumes, candidates will generally know sooner whether they’ve progressed to an interview or are no longer being considered for a position. Less wait time means a better candidate experience and increases the chances that an individual may reapply in the future for a position they may fit better. 

Related: What Is Candidate Experience (and How to Create a Positive One)

Reduced risk of unconscious bias

With the right criteria, AI can reduce unconscious bias in your hiring process. You can teach an AI system to ignore things like names and addresses and focus only on other data. As a result, machine learning for resume screening can lead to a more diverse pool of candidates when deployed correctly.

Cons of resume screening using machine learning

For all its benefits, AI resume screening does have some drawbacks, which may include the following.

Risk for AI bias 

Although AI doesn’t hold biases of its own, there’s a risk it may learn them from humans. If given an initial data set that includes a largely homogeneous group in terms of background, the AI system may exclude more diverse candidates. 

AI may also develop biases of its own if not trained properly. For example, if the majority of your candidates come from your local area, an AI system may mistakenly identify your city as a keyword and not move forward individuals looking to relocate to your city.

Possibility of false negatives and positives

The short list generated by AI may not always be flawless. You can end up with false positives  and resumes that make it through the cut but don’t meet your standards. False negatives can also occur, allowing quality candidates to slip through the cracks.

Less opportunity for relationship building

Relationship building, the process of developing a pool of quality candidates you can draw from when positions open, is vital to proactive hiring. When you use AI to screen resumes, only the short-listed candidates are likely to receive personal messages from your recruiting team. While this saves time, it also means you won’t have the opportunity to start relationships with the other applicants who may be well-suited to positions you’ll need to fill in the future.

Best practices for deploying AI resume screening

When introducing AI to your resume screening process, consider these tips to support seamless integration and maximize the benefits of the technology. 

Start small

Although you’re likely eager to begin streamlining your hiring process with AI, consider resisting the urge to begin using your new system for all of your hires. Start with one or two positions so your team can learn how to use the system and you can more easily assess the results. As you grow more comfortable and the system begins to learn based on feedback, gradually expand usage.

Select the right sample set

When training your AI for resume screening, you’ll first need to teach the system what a quality candidate is.  That requires providing it with an initial sample set, a group of resumes that a human reviewer identifies. As you put together resumes for the set, prioritize inclusiveness. Providing the system with a range of quality candidates with a broad spectrum of experience and education can foster diversity and lower the likelihood of bias.

Rethink your job descriptions

Your job descriptions will also help to inform your AI screening tool, so take the opportunity to reexamine them and add more inclusive language. Determine how well the description reflects the daily duties and responsibilities the position entails and whether the skills, qualifications, education and experience requirements you have set are all actually important for a candidate’s success. 

For example, you may currently say that a bachelor’s degree is mandatory for consideration, but in the past, recruiters have waived this requirement for individuals with many years of experience. By updating the description with inclusive language, you make it more likely for the AI to identify quality candidates.

Related: How to Write a Job Description

Provide regular feedback

Without feedback from human users, AI screening systems can’t improve their algorithms and get better at identifying quality candidates. To get the most out of the system, use the provided assessment tools to rate its performance. Also, update your data set with new resumes on a regular basis, and redefine your data set any time a job description changes. 

Adopt a hybrid screening model

When switching to resume screening using machine learning, your objective shouldn’t be to eliminate people from the process entirely. Instead, view your screening system as an AI partner, a tool that can simplify the work of your recruitment team. A hybrid approach that involves humans training, monitoring and refining the results produced by an AI system can lead to better hiring decisions. 

AI Resume Screening FAQs

How do I choose an AI resume screening system?

To decide which AI resume screening system is right for your business, consider which type it is. Compare the features available, looking for a system that’s simple to set up, provide feedback to and audit. Consider the availability of tech support to ensure someone will be available during your working hours to answer questions and assist users with problems. Whether the software developer offers tutorials or training classes to help your team learn to use the AI is another key point of comparison.

How do I assess the effectiveness of an AI resume screening system?

Tracking false positives and negatives over time is one way to assess your AI resume screening tool, as the number should decrease as you provide feedback. You can also track the performance and retention of hires made with the assistance of AI. If you find that many hires who seemed to be quality candidates based on AI review aren’t meeting performance expectations or that turnover increases, your system likely needs an overhaul.

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