AI and Bias Mitigation in HR: Using Machine Learning for Fair and Inclusive Hiring Practices

Abstract

Artificial intelligence (AI) became increasingly essential to human resources processes (HR), particularly in recruitment and hiring. Although AI-oriented tools offer the potential to increase efficiency and objectivity, they also risk perpetuating or expanding historical biases incorporated into training data. This study explores the application of machine learning algorithms (ML) to detect, reduce, and mitigate the bias in HR decision making, with a specific focus on hiring practices. We analysed various ML techniques with recognition of justice, such as restrictions on re-advertising, opponent debiasing, and justice to improve algorithmic equity in gender, race, and other protected attributes. In addition, we present a structure to integrate the Explainable AI (XAI) to increase transparency and confidence in AI-oriented hiring systems. The article includes a case study that demonstrates the effectiveness of bias mitigation strategies in a real-world hiring data set. Our findings point out that the ethical implementation of AI in HR requires technical innovation and strong governance to ensure compliance, inclusion, and justice. The proposed model contributes to the development of responsible and inclusive hiring systems that balance performance with ethical responsibility.

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