AI-Driven Recruitment and Hiring Automation through CV Scanning and AI Agents

Abstract

The presented work is dedicated to the development
of an AI agent that will perform automated CV sorting and
evaluation to assist current hiring processes with a supervised
machine learning model. A Random Forest Regressor containing
300 trees was trained on structured features based on the
unstructured CV information including years of experience,
number of related skills, education level, overlap of skills with the
job description, and coverage ratio. The 50 actual CVs dataset
was divided to 80 percent training set and 20 percent testing
set and the ground truth target used in supervised learning
was HR inspired rule based scoring. The model realized an
impressive predictive power, as seen with the R² of 0.94 and Mean
Absolute Error of 4.46 which signifies a significant agreement
with human based ratings. The trained model is integrated to
an AI agent that automates the ranking of the candidates, short
listing and scheduling of interviews. The system is particularly
effective at unloading HR staff and increasing the efficiency of
the recruitment process; nevertheless, some of the key issues are
the bias in the training data, the tendency of the system to treat
different candidates with unequal consideration, and the limited
interpretability of the model. The study highlights the importance
of introducing feedback loops, reducing bias, and establishing
transparency to ensure that AI-based recruitment systems were
ethically, reliably, and humanly deployed.