A Comparative Study on Efficiency, Accuracy, and Fairness in Talent Acquisition

Abstract

The swift digital transformation of human resource management has profoundly changed 
recruitment processes, including the widespread use of Applicant Tracking Systems (ATS) and 
software for automating resume screening. These systems not only enable greater efficiency but 
also allow handling large volumes of work. On the other hand, issues of fairness, transparency, 
and quality of decisions remain a matter of concern. The paper seeks to explore the relative 
merits of human and algorithmic resume screening in the context of hiring. 
A mixed-method research design has been employed for this study. Quantitative data were 
collected via the distribution of structured questionnaires, while qualitative data were collected 
through interviews with HR professionals. The study looks at four main variables: screening 
efficiency, accuracy, fairness, and Decision Quality. Statistical analysis, including descriptive and 
inferential methods, was used to interpret data. 
It was found that algorithmic screening can lead to a more efficient and consistent process, 
especially when there are lots of applicants. Nevertheless, humans are still better at grasping the 
contextual and qualitative elements of candidate profiles. The research points to the fact that a 
combination of human decision-making and algorithmic assistance results in a fairer and more 
effective hiring outcome. 
The study adds to the evidence supporting human-AI partnership in recruitment, and it serves as 
a guide for companies intending to craft fair, efficient, and technologically advanced talent 
acquisition strategies.