AI in Hiring: Leveraging Machine Learning for Fair, Efficient Recruitment
Bibliographic record
Abstract
Adopting new technologies changes how people once worked and performed, guaranteeing they get the best out of it while maintaining their leadership in their respective industries. Recently, the HR industry has started using machine learning (ML) as a way to become more innovative in their work. ML has been incorporated into many organizations to support data-based decisions in various areas, including the recruitment process. Traditional methods include the long process of filtering and analyzing resumes in order to identify suitable candidates. Furthermore, personal biases play a role in these selection processes, which may hinder their compatibility with the position. Another factor to consider is the inconsistency of evaluation criteria during the hiring process. Using ML-based techniques, the evaluation of all candidates is done in a shorter time, with more structured and evidence-based approaches utilized. The objective of this chapter is to give a deep understanding of applying ML technology in the candidate selection process and how it enhances the stages of the hiring process. It also highlights the benefits and challenges of using ML. A suggested model of how ML can be applied in practice to reduce bias in candidate selection will also be discussed. It also offers practical recommendations for HR professionals when applying ML techniques, training, and monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".