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  • Writer's pictureLisa Dupras

The Impact of Resume Review AI Screening on Your Job Search

Updated: 3 days ago

Some of you may have noticed the terms candidate relevancy or candidate match creeping into ATS application processes. Specifically, the ADP ATS has introduced the Artificial Intelligence (AI) based calculation of a Candidate Relevancy Score and applicants need to opt in or out of the process. The Workday ATS and others have similar improvements planned. Should you allow AI-powered scoring of your resume or is better to opt out? Let's break it down.


What is Candidate Matching? 

Candidate relevance is the degree to which a candidate profile aligns with the specific requirements of an open job and the process by which recruiters identify suitable candidates from a pool of applications. The process has been around since the 90s.


Talent professionals additionally use other factors like soft skills, cover letters, experience, education, soft skills, skills tests, coding challenges, and video interviews to assist in their assessments. Each company uses a slightly different algorithm to assess candidate match. For example, Microsoft might require a coding challenge for computer programmer positions, and Amazon may not.


Robotic finger on a keyboard

AI Is Changing the Job Search Landscape

In the past ten years, Applicant Tracking Systems have introduced more automated capabilities for searching candidate databases by identifying relevant keywords and skills for more efficient candidate selection. The advent of AI and machine learning has started to change the landscape of recruitment processes:


  • Assessing candidate relevancy scores and match prediction for interview selection

  • Use of AI screening tools and AI systems

  • Social media screening for red flags

  • Evaluating video interviews for job match assessment

  • Use of personality or aptitude tests to assess cultural fit.


Machine Learning (ML) Used in Resume Review AI Screening

The ADP ATS uses large language models (LLM) and Machine Learning (ML) to assign a Resume Review AI Relevancy Score by assessing how well a candidate's education, skills, and experience match a posted job. Each of the three areas receives a separate score, which is then weighted and summed. Weighted adjustments are assigned by the company and not shared with applicants. ADP states that the Candidate Relevancy score will not replace recruiter reviews or final candidate decision-making. Candidates may opt out of this process. ADP additionally shares statistical evidence of no gender or ethnicity bias.

 

How can Large Language Models (LLM) Enhance the Resume Review AI Process?

LLMs process the nuances of language, assessing the meaning and relationships between words and concepts. They can also interpret complex information not explicitly stated in a resume. Therefore, using LLMs should provide a more finely tuned Candidate Relevancy Score than just keyword and skills matching.


LLMs can significantly enhance the accuracy and efficiency of resume screening by understanding context and intent. For example, they can distinguish between similar job titles with different responsibilities or recognize industry-specific jargon and acronyms. This deeper understanding enables the AI to more accurately match candidates to job requirements, reducing the likelihood of overlooking qualified applicants due to semantic nuances. LLMs are steadily getting better at evaluating resumes.


LLMs are learning from vast datasets, continuously improving their ability to predict candidate success based on previous hiring outcomes. They can also identify soft skills and personality traits inferred from the language used in resumes, providing a more holistic view of a candidate's potential fit within a company's culture. This advanced analysis goes beyond surface-level matching. Companies are just starting to see this functionality in newer ATSs.

 

How can ML Enhance the Candidate Screening Process?

Machine learning uses historical data to predict the likelihood of candidate success in a role based on previously identified patterns and relationships in past hirings. These predictive learnings adjust the Candidate Relevance Score.

 

For example, a job searcher applies for a Customer Service position. It requires excellent communication, problem-solving ability, and experience working in a fast-paced environment.


  • The LLM scans the resume for keywords related to communication skills, such as conflict resolution.

  • The ML model analyzes past hiring data and identifies patterns, like a higher success rate for candidates with previous call center experience.

  • Resumes showing customer service ability not captured by ML-identified keywords or without traditional call center backgrounds would receive lower Candidate relevance scores.


The theory is that the ML model will learn and improve over time to use more finely tuned keywords, and recruiter reviews will catch and include resumes with lower scores.

 

How Does the ATS Assign a Candidate Score?


Resume Submission:

  • The candidate submits their resume to the ATS, which serves as the initial point of contact between the applicant and the hiring organization.

Data Extraction:

  • The Large Language Model (LLM) processes the resume by extracting all relevant and contextual information. This includes not only explicit details like job titles, skills, and educational background but also implicit information such as inferred skills, experience levels, and nuanced language cues.

Contextual Analysis:

  • The LLM goes beyond basic keyword matching by understanding the meaning and relationships between words and concepts. This allows it to interpret complex information that may not be explicitly stated in the resume, such as industry-specific jargon, project outcomes, and contextual job roles.

Predictive Modeling:

  • The Machine Learning (ML) model then analyzes the extracted data, leveraging historical hiring data to predict the candidate's potential success in the applied role. This includes assessing how previous candidates with similar profiles performed in similar roles, considering both hard skills and soft skills.

Candidate Relevancy Score:

  • The insights gained from the LLM and ML model are combined to produce a final candidate relevancy score. This score reflects how well the candidate's profile matches the job requirements, taking into account both the quantitative data (such as years of experience and qualifications) and qualitative data (such as inferred competencies and cultural fit).

Prioritization for Review:

  • Recruiters use these scores to prioritize candidates for further review and interviews. Candidates with higher scores are deemed more likely to succeed in the role and are therefore given precedence in the hiring process. This prioritization helps streamline the recruitment process, ensuring that the most promising candidates are identified and engaged promptly.


Benefits of Using LLMs and ML in ATS

By integrating LLMs and ML into the ATS, organizations can enhance the accuracy and fairness of their hiring processes. LLMs ensure a deeper and more nuanced understanding of candidate profiles, while ML models leverage historical data to make informed predictions about candidate success. Together, these technologies help recruiters make data-driven decisions, reduce bias, and improve overall hiring efficiency. This results in a more effective matching of candidates to job roles, ultimately contributing to better organizational outcomes.


Note: The Use of LLMs and Machine Learning for candidate relevancy scores should exhaustively tested for bias before use. Candidates should confirm this bias testing when making any opt-in or opt-out decision.

 

Should I Opt-Out of AI Resume Screening?

 

Opting In:

  1. Priority Based on Job Requirements - Resumes that closely match the job requirements are likely to receive higher priority in the AI screening process. This can streamline the hiring process, ensuring that candidates who meet the essential criteria are quickly identified.

  2. Streamlined Screening Process - Opting in can save time and effort as the AI may skip some of the initial screening steps. This efficiency is particularly beneficial for recruiters handling a high volume of applications.

  3. Feedback and Position Matching - AI-driven platforms often provide instant feedback on how well a resume matches the job description. This can help candidates understand their standing and make necessary adjustments to improve their chances.

  4. Exposure to Selection Bias - While AI screening aims to be objective, it is influenced by algorithms and data inputs that may include unseen biases. Opting in means the candidate’s resume is subject to these biases, which can impact the evaluation process.

  5. Lack of Transparency - Companies typically do not disclose the specific factors that affect relevancy scores in AI systems. This lack of transparency can make it challenging for candidates to understand why their resume scored a certain way.

  6. Data-Driven Decisions - The assessment of candidate relevance in AI screening is heavily data-driven, relying on patterns and historical data rather than human intuition. This approach can enhance consistency but may overlook nuances that a human recruiter might catch.


Opting Out:

  1. Bypassing AI Bias - By opting out, candidates avoid potential biases introduced by AI algorithms. This can be advantageous for those with unique skills or experiences that may not align perfectly with typical keyword matches.

  2. Human Review Advantage - Resumes that highlight distinctive skills and experiences may be better received by human recruiters. Human reviewers are often more adept at recognizing value beyond the data points, such as creativity, leadership potential, and cultural fit.

  3. Deprioritization by AI - Resumes that opt out of AI screening might be deprioritized by ATSs that rely heavily on AI. This could result in a longer wait time for review or even exclusion from the initial candidate pool.

  4. Rigorous Screening Process - The screening steps for opted-out resumes are typically more rigorous and can take longer. Recruiters may manually review these resumes, ensuring a thorough evaluation but possibly increasing the time to hire.


Opting In/Opting Out Key Considerations:

  • Unique Skills and Experiences - If your resume showcases unique skills or unconventional experiences, human review might be more beneficial. Recruiters can appreciate the subtleties and qualities that AI might miss.

  • Industry Practices - Consider the norms in your industry. Some fields heavily rely on AI screening, while others may prioritize human judgment. Researching the hiring practices of your target companies can provide insights.

  • Feedback Opportunities - Opting in may offer quicker feedback on your resume’s alignment with job descriptions, while opting out might allow for a more personalized review process.


In conclusion, whether to opt-out of AI resume screening depends on your specific situation, the industry standards, and the nature of your qualifications. Weighing these factors can help you make an informed decision that maximizes your chances of landing the job.


AI-Powered Candidate Scoring Pros and Cons


  • Job searchers strongly concerned about bias or lack of company transparency around the hiring process may want to opt-out.

  • Suggest opt-out if the candidate review process only utilizes AI (and no human review).

  • Suggest opt-in if the resume is a clean match and the job searcher wants increased visibility.


If the job searcher submits a quality well-matched resume, relevancy should not be problematic for visually OR digitally reviewed resumes! There is an open question about how a company's internal recruitment processes will evolve, with some candidates choosing to opt in and some out. As AI models are trained and machine learning improves its models, the results should improve and bias risks reduced, but only time will tell. For more info on ADP’s Candidate Relevancy Score click on the following link to ADP for more information.


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