10 min read

AI In Resume Screening: Expectations vs. Reality

Hiring managers across industries and at companies of all sizes are struggling with the same issue: finding great candidates. According to Zety, a recruiter receives more than 250 resumes for an open position, on average — only 12% meet the requirements for the position. 

Yet, there are many qualified workers searching for jobs. A recent report from Harvard Business School found that top candidates are becoming more “hidden” to recruiters than ever before

“Our analysis indicates many such workers want to work and are actively seeking work,” wrote the study’s authors. “They experience distress and discouragement when their regular efforts to seek employment consistently fail due to hiring processes that focus on what they don’t have (such as credentials) rather than the value they can bring (such as capabilities).” 

HBS estimates that there are more than 27 million hidden workers in the US alone. Ultimately, therefore, the challenge for hiring managers is to ensure well-qualified candidates seeking employment are included in the recruitment process. 

Helping hidden gems enter and proceed through the hiring process starts with better screening. Resume screening is one of the most labor-intensive and manually exhausting parts of talent acquisition. AI in resume screening is now on the rise, with many tools promising to improve the selection process.

In this article, we’ll take a look at why AI Resume Screening expectations might not meet reality. We’ll also highlight challenges to be aware of, and innovations that go beyond relying on resume at all.

Recruiters overwhelmed with resumes
Recruiters receive an average of 250 resumes per role, with only around 12% meeting the role requirements, according to Zety

What is AI in resume screening?

Artificial intelligence (AI) can be used throughout the hiring process to help hiring teams improve their time to hire, quality of hire, save time and money, and minimize some of the hidden human bias that prevents unconventional candidates from getting a fair evaluation. 

AI tools typically target three stages of the hiring process: 

  1. Sourcing: finding and connecting with talent quickly
  2. Screening: quickly deriving the best applicants
  3. Interviewing: facilitate remote hiring and save time

AI screening tools range from resume parsing to behavioral and skill assessments

AI resume screening is the practice of using artificial intelligence to sort through resumes and applications and move the best candidates to the next round of the recruitment process. AI resume screening tools attempt to streamline the time-consuming process of sorting through resumes to find qualified candidates. 

How companies use AI resume screening tools

Hiring managers spend a huge amount of time screening resumes as part of the traditional hiring process. By some accounts, resume screening can take up to 23 hours for just one hire.

Historically, candidate screening was a time-consuming, manual process. A hiring manager would receive a stack of resumes or applicants and spend time reading each one to find the right candidates to offer a skill assessment or phone screen. For a particular job opening, there are likely to be around 250 resumes for the hiring manager to sort through. 

The rise of high-volume hiring has made manually managing the resume screening process virtually impossible. And, as more recruiters start to realize that unconscious bias is impacting their hiring success, companies are turning to machine learning and recruitment software to help with screening candidates fairly.

How do resume screening tools work? 

Resume screening tools use machine learning algorithms to parse the information in PDF or Word files. Essentially, the applicant uploads their resume into the parsing tool. The artificial intelligence then scans each document and extracts information relevant to the hiring manager’s needs, such as the candidate’s skills, experience, skills, qualifications, etc.

Resume screening tools typically fall into one of three categories: 

  1. Keyword-based: artificial intelligence screens for keywords, phrases, and patterns in the text to sort candidates. 
  2. Grammar-based: machine learning algorithms use a list of predefined grammatical rules, breaking down words and phrases on the CV to understand the meaning of each sentence. 
  3. Statistical: numerical models analyze the information on a resume, recognizing structures such as addresses, timelines, and the meaning of specific words. 

Statistical tools are the most advanced ways to screen resumes. With each category, hiring managers can set custom criteria to search for to make sure qualification indicators are set up to find the right candidate. 

Main categories ai screening tools fall into
Main categories AI screening tools fall into

Expectations of using AI tools in screening

Improve candidate shortlist

Resume screening has historically been one of the first steps in the traditional hiring process. As a result, this step often results in a candidate shortlist based on relevant information matching the job description. This shortlist then determines who is eligible for scheduling interviews, completing a skill assessment, or meeting with human recruiters.

Unfortunately, eye-tracking shows that recruiters spend about seven seconds reviewing each resume — less if there are more resumes than usual. Seven seconds is simply not enough time to get the full picture of someone’s capabilities, which can lead to recruiters selecting candidates based on random (or biased) criteria. AI screening tools use screening criteria that are pre-set and standard across every resume to develop a more consistent, quality candidate shortlist. 

Reduce unconscious bias

Artificial intelligence (AI) in recruitment can be a double-edged sword. Used ethically, AI is a tool for good that provides powerful, bias-free data points. However, it’s also true that ​​machine learning is susceptible to human hiring bias introduced through the data set. 

In the resume screening process, it’s important to ​​clean the data, removing names (ethnic bias), location (geographic or socioeconomic bias), gender (gender bias), date-of-birth (age bias), and so on. This ensures that new applicants are considered based on their credentials, rather than other heuristics. AI tools native to an applicant tracking system can often clean data as well as parse resumes. 

Hire at scale

AI tools help level the playing field for small businesses to compete with larger enterprises. Merchants leveraging AI can evaluate applicants at scale, in days not weeks, without compromising quality. These screening tools can also not only replace manually reading resumes but also phone screens — which serve virtually the same purpose as a resume review. For companies managing multiple open positions at once, this double-ended benefit can help hiring managers and recruiters spend time on the candidates most likely to continue through the application process (without screening anyone out). 

Risks of using AI tools in screening

As noted above, there are some risks to using AI in screening. 

If not implemented properly, AI tools can replicate some of the same biases that recruiters bring to the resume review. AI systems are really only as good as the data used to train them by the people who built them. When hiring teams use historical data to train machine learning tools, such as data collected from the company’s internal database, then the system inherits the biases of recruiters who previously vetted those candidates. 

And, AI isn’t a perfect solution, as it can only parse the data that’s available. Resumes are a notoriously bad representation of a candidate’s true ability. Not only are they imbued with gender and racial biases, but they also focus on style, rather than substance. Resume parsing tools can only assess the candidate based on the information represented, meaning that if a candidate hasn’t mentioned valuable information — or worse, lied about their qualifications — the AI solution will misattribute their qualifications.

Tips and tricks for using AI in screening

While AI can save recruiters time and effort, clearly it does take some training and setup. Here are some tips for implementing artificial intelligence into your resume screening process.

Set the right criteria

Cleaning data is one way to implement blind hiring. Setting the AI screening tool to scan the resume for the right criteria is also an important step. Most AI resume screening tools use proxies such as college degrees for work ethic or productivity. But, these proxies result in many well-qualified candidates being excluded from consideration.

“Workers are excluded from consideration due to variables such as the lack of a college degree or a gap in their employment history. While employers may infer that applicants who have those attributes are undeserving of consideration, applying an ‘affirmative’ logic would seem a more logical approach for seeking talent,” according to the Harvard Business School

Ultimately, you should look for ways to screen candidates in, rather than screen them out. 

Define the role of a resume screening tool

There’s a specific time and place to use a resume screening tool. Resume screening is only the first step in the hiring process and should be treated as such. The true value of a resume screening system is to make sure that spam applicants and those who are truly unqualified are eliminated as quickly as possible, allowing recruiters to focus on candidates who have a shot at joining the company. 

As you consider your use of AI in screening, try to see it as a tool, rather than a complete solution. It can speed up screening and reduce manual effort while giving hiring managers time to spend interviewing and vetting candidates on a more personal level.

Rethink screening beyond resumes

Some solutions go above and beyond simply narrowing down a pool of applicants based on their resumes. For example, skills assessments from software such as Vervoe and Vervoe’s AI can be sent out to prospective candidates to identify their current skills and produce a final ranking that suggests whether they would actually be a good fit for a role. 

Rather than just assuming skills based on previous experience listed on a resume, you can actually put applicants to the test and take the guesswork out of hiring. 

Tech alternatives to resume screening
Vervoe’s innovative AI makes it a great alternative to resume screening solutions

Conclusion

Resume screening has long been the first step in the hiring process, but it’s often the most time-consuming and on of the least effectives way to find great candidates. For recruiters that are frustrated by manually sorting through hundreds of resumes to find those hidden gems, an AI tool can help reduce friction by screening in qualified candidates and removing resumes that don’t meet the job description. 

When trained properly, AI resume screening tools can help reduce human biases, elevate top talent, and streamline the process so that candidates and recruiters alike have a better experience. Companies should avoid over-relying on resume screening in general, and use this step in the greater context with skills assessments and interviews. 

To learn more about AI and machine learning in recruitment and hiring, check out Vervoe’s AI-Powered Automation and read our guide to AI + Recruiting Automation: Ways You Need To Use Them Now.  

Emily Heaslip

Emily Heaslip

Emily Heaslip is a versatile freelance copywriter who writes for finance, tech, and e-commerce brands. She currently lives in Cape Town and can be found running, hiking, and exploring the South African coast in her free time.

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