A Comprehensive Review of AI Techniques for Addressing Algorithmic Bias in Job Hiring

Algorithmic bias in job hiring has become a critical concern with the increasing use of AI in recruitment processes. This review explores various AI techniques aimed at mitigating bias, particularly in the initial stages of candidate screening, such as CV review. Traditional recruitment systems often perpetuate bias due to reliance on historical data, which may be skewed by human prejudices. Through an in-depth analysis of Natural Language Processing (NLP) and deep learning methodologies, this paper discusses innovative approaches to debiasing, including techniques like adversarial training, fairness constraints, and explainable AI. These approaches attempt to reduce bias while maintaining the efficacy of AI- driven recruitment tools. Furthermore, this review evaluates the current limitations of these techniques and highlights the challenges in balancing fairness, accuracy, and transparency. By addressing these issues, the goal is to foster a more equitable recruitment process that is both effective and unbiased.