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 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.
AI Is Changing the Job Search Landscape
In the past ten years, ATSs 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
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 ATS Resume Screening
The ADP ATS uses large language models (LLM) and Machine Learning (ML) to assign a Candidate 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 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.
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?
Job searcher applies and submits their resume to an ATS.
The LLM processes the resume by extracting all relevant and contextual information
The ML model analyzes the extracted data and predicts success using past hiring data.
Insights are combined to produce a final candidate relevancy score.
Recruiters use the scores to prioritize candidates for further review/interviews.
Should I Opt-Out of AI Relevancy Screening?
Resumes could receive priority if they closely match the job requirements.
The process might skip some initial screening steps, saving time and effort.
It may be easier to receive feedback about a resume to open a position match.
The candidate's resume is additionally exposed to selection bias for unknown factors. However, opting out will expose the candidate's resume to potential human review bias.
Companies do not share the factors that can adjust relevancy scores.
The assessment of candidate relevance relies very strongly on data rather than intuition.
Potential bias introduced by LLMs is bypassed when resumes are reviewed solely by recruiters.
Resumes that highlight unique skills and experiences may fare better with human review.
ATSs that rely heavily on AI-based screening may deprioritize opted-out resumes.
Screening steps may be more rigorous and take longer.
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 here.