ALGORITHMIC APPROACHES TO AUTOMATED CANDIDATE SCREENING

Abstract

The rapid transformation of business processes worldwide through the application of machine learning models is one of the key methods for increasing efficiency. In this situation, the most important necessity is using algorithmic approaches to automate the recruitment process. In this article, we present an in-depth study of algorithmic approaches, which can be used to automate candidate screening, with the primary objective of ensuring the effectiveness and sustainability of the hiring process together with addressing prejudice, subjectivity and inconsistency issues of human-based screening. This study aims to clarify how strategic process improvements can be made by applying various algorithmic approaches and machine learning techniques.

Algorithmic approaches to automated screening are transforming the initial stages of candidate selection, promising enhanced efficiency and scalability in managing large applicant pools. This article is focused on a scientific review of these algorithmic methods, categorizing and analyzing rule-based systems, natural language processing (NLP) techniques, and machine learning (ML) classification algorithms. It evaluates their capabilities, methodologies, and performance metrics, including accuracy, precision, and fairness, and discusses empirical findings from real-world applications. The review underscores critical identified challenges of data dependency, the potential for bias amplification, the need for algorithm transparency, and following rules for personal data processing.  Future research directions are proposed, emphasizing the development of robust, fair, and explainable algorithms to ensure responsible and effective automated candidate screening in recruitment.