Algorithms for Fair Hiring: A Review of Techniques for Detecting and Mitigating Bias
Aarushi Singh, Akshita Goel, Jyotika Jaichand, Vaishali Kikan, Ashwni Kumar · 2024
This study examines the use of machine learning to detect and mitigate biases in the hiring process, with a focus on gender, racial, age, and socioeconomic biases. We conduct a thorough review of existing research to analyze various machine learning models and bias mitigation techniques employed in recruitment. Our findings reveal that while machine learning can enhance efficiency in hiring, it can also reinforce existing biases if not properly managed. Techniques such as threshold adjustments and ensemble methods demonstrate potential in reducing discrimination but may compromise accuracy. The study highlights the importance of transparency, accountability, and continuous monitoring in the implementation of these systems. Integrating human oversight is crucial to ensure fairness and inclusivity. Future research should prioritize refining fairness metrics, expanding bias categories, and developing real-time bias mitigation strategies.