The Impact of AI-Generated Resume Review Scores on Your Job Search

FORKED RIVER, N.J. - April 15, 2024 - PRLog -- Some of you may have noticed the terms candidate relevancy or candidate match creeping into ATS application processes. Should you allow AI-powered scoring of your resume or is better to opt out?

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.

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:

1) Assessing candidate relevancy scores and match prediction for interview selection
2) Social media screening for red flags
3) Evaluating video interviews for job match assessment
4) Use of personality or aptitude tests to assess cultural fit.

HOW MACHINE LEARNING USED IN AI RESUME SCREENING

The ADP ATS uses large language models (LLM) and Machine Learning (ML) to assign a Candidate Relevancy Score by assessing a candidate's education, skills, and experience match a posted job. Each of the three areas receives a separate score. Weighted adjustments are assigned by the company and not shared with applicants. Candidate Relevancy score should not replace recruiter reviews or final candidate decision-making. Candidates may opt out of this process.

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?

1) Job searcher applies and submits their resume to an ATS.
2) The LLM processes the resume by extracting all relevant and contextual information.
3) The ML model analyzes the extracted data and predicts success using past hiring data.
4) Insights are combined to produce a final candidate relevancy score.
5) Recruiters use the scores to prioritize candidates for further review/interviews

AI-POWERED CANDIDATE SCORING PROS AND CONS

1) Job searchers concerned about bias or lack of company transparency around the hiring process may want to opt out.
2) Suggest opt-out if the candidate review process only utilizes AI (and no human review).
3) 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! As AI models are trained and machine learning improves, the results should improve and bias risks reduced, but only time will tell.

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