Pitfalls of Machine Learning-Based Personnel Selection

Abstract. In recent years, machine learning (ML) modeling (often referred to as artificial intelligence) has become increasingly popular for personnel selection purposes. Numerous organizations use ML-based procedures screening large candidate pools, while some companies try automate the hiring process far possible. Since ML models can handle sets of predictor variables and are therefore able incorporate many different data sources more than common consider), they promise a higher predictive accuracy objectivity in selecting best traditional personal processes. However, there pitfalls challenges that have be taken into account when using sensitive issue selection. this paper, we address these major – namely definition valid criterion, transparency regarding collected decision mechanisms, algorithmic fairness, changing conditions, adequate performance evaluation discuss recommendations implementing fair, transparent, accurate algorithms.