Data diversity and its impact on machine learning fairness

Abstract

Machine learning algorithms are increasingly deployed across various domains, influencing critical decisions in finance, healthcare, education, and criminal justice. As these systems impact more aspects of human life, ensuring their fairness has become imperative. Data diversity, a crucial element in achieving fairness, encompasses the inclusion of varied data points representing different demographics, socio-economic backgrounds, and scenarios. This research article explores the importance of data diversity in machine learning, examines its impact on model fairness, and discusses strategies for fostering diversity in datasets to enhance the equitable performance of machine learning systems.