Research of Explainable Machine Learning Models for Candidate Evaluation and ıts Influence on Trust in Automated Hiring Systems

The study explores the application of machine learning models in automated recruitment systems. We evaluated the performance of linear regression and decision tree models in candidate selection using Python and the scikit-learn library. The research highlights key features influencing model decisions and proposes a mathematical framework to quantify the impact of individual factors on candidate assessment. The developed model adheres to data privacy requirements, including compliance with the European Union General Data Protection Regulation, ensuring transparency and protection of personal data. Results demonstrate the high efficacy of machine learning in recruitment, with specific emphasis on mitigating algorithmic bias while maintaining predictive accuracy.