Predicting Hiring Decisions Using Machine Learning: A Transparent and Fair Approach for Human Resource Management
DOI:
https://doi.org/10.65405/bb84r647Keywords:
Human Resource Management, Recruitment, Hiring Decision Prediction, Machine Learning, Explainable Artificial Intelligence, Fairness Analysis, XGBoost, Human Resource Analytics.Abstract
(Times New Roman: size - 10) Abstract must be written in English within 300 words.
This study explores how machine learning can help predict hiring decisions in Human Resource Management (HRM). We tested four different classification algorithms—Logistic Regression, Decision Tree, Random Forest, and XGBoost—using a recruitment dataset from Kaggle. Beyond just predicting who gets hired, our research also focused on making these AI systems understandable (Explainable Artificial Intelligence, or XAI) and fair. Our findings show that advanced machine learning models, particularly ensemble methods, performed better than simpler ones. Crucially, our work on explainability and fairness addresses key limitations found in earlier research, paving the way for more responsible AI in hiring.
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