A Comprehensive Strategy to Bias and Mitigation in Human Resource Decision Systems

In recent years, Machine Learning (ML) and Artificial Intelligence (AI) models have become integral to various business operations, especially within Human Resource (HR) systems. These models are primarily used to automate decision-making processes in recruitment, performance assessment, and employee management, enhancing efficiency and streamlining tasks. However, the increasing use of these automated systems has raised significant concerns about the presence of bias, which can lead to discriminatory practices. Such biases may exclude qualified candidates and diminish opportunities, while also posing substantial risks to a company’s reputation, with potential legal and ethical consequences. This paper addresses these challenges by exploring the root causes of bias in HR-related ML models and proposing best practices for mitigation. It presents a thorough examination of fairness concepts and definitions within the context of HR decision-making, emphasizing the complex nature of selecting appropriate mitigation techniques based on the specific models and datasets used. Through an empirical evaluation of various mitigation strategies, the study reveals that no single approach can fully satisfy all fairness metrics, highlighting the inherent trade-offs between accuracy and fairness. The findings offer valuable insights into optimizing these trade-offs and provide actionable recommendations for achieving fairer, unbiased outcomes in automated HR systems. Additionally, this research underscores the ongoing need for further study and discussion to enhance transparency and fairness in ML models, contributing to a more equitable HR landscape.