Machine Learning Bias in Resume Screening

Abstract

Machine Learning (ML) has revolutionized hiring processes, introducing new dynamics and challenges, especially in the realm of resume screening. This study delves into the challenges and implications of ML-driven resume screening, specifically addressing gender bias. We utilized a dataset containing resumes from different sectors in India to train models, including Random Forests and Multilayer Perceptron Classifier. These models were employed to categorize resumes into sectors such as technology, business, etc. Our results reveal gender biases, with men more frequently predicted as executives and women in technical roles, reflecting historical disparities. Furthermore, we identify limitations and ethical concerns surrounding such classifiers, emphasizing the need for responsible AI deployment in recruitment processes. By shedding light on the complexities of bias in ML-based recruitment, this research contributes to the ongoing discourse on ethical AI deployment, offering insights and recommendations for fostering fairness and accountability in hiring-based automated decision-making processes.