Machine Learning Advancing Diversity Equity and Inclusion in Data-Driven HR Practices

Abstract

This work explores the transformative potential of Machine Learning (ML) in advancing Diversity, Equity, and Inclusion (DEI) within data-driven Human Resource (HR) practices. ML addresses systemic inequities in recruitment, promotions, and workplace engagement through tools that identify biases, automate fairness-focused evaluations, and analyse inclusion metrics. While offering unparalleled efficiency and scalability, ML introduces algorithmic bias, ethical complexities, and regulatory compliance challenges. Organisations can foster transparency, trust, and accountability by integrating fairness-aware ML models, real-time DEI dashboards, and federated learning. This framework underscores a human-centred, collaborative approach, ensuring ML aligns with DEI principles and promoting equitable workplace cultures globally. The study highlights best practices, ethical considerations, and interdisciplinary strategies to balance innovation with fairness, paving the way for a future where technology drives inclusivity