Recruitment and Selection Processes in Campus Placements with Machine Learning

Campus recruitment and selection processes that typically rely on various parameters and interviews which are undergoing transformational change through the integration of machine learning (ML). This paper explores the growing impact of ML algorithms in campus placement on various aspects of deployment, and the potential benefits. We know how ML algorithms can streamline resume screening, automate shortlisting of candidates based on skills and cultural fit, and predict future performance through data-driven assessments. In addition, the paper addresses ethical considerations of bias in algorithms, the need for transparency in decision-making, and the role of humans in the final choice. By examining case studies and empirical research, we investigate the impact of ML on campus implementation. We set out future directions for responsible and effective ML integration, and pave the way for more efficient, data-driven and equitable talent acquisition across universities and companies.