Exploring Equity: Integrating Knowledge Graphs in Fairness Testing Methodologies

In the Big Data era, machine learning and artificial intelligence have reshaped decision-making, especially in sectors like healthcare and finance. However, this evolution raises significant concerns about fairness and bias. The abundance of data increases the risk of inherent biases, exacerbating social inequality and discrimination. Ensuring ethical machine functioning, where every user receives equal treatment, is essential. Conventional methods for handling large datasets struggle to identify and rectify biases, as big data often equals skewed data. To address this, we propose a knowledge graph-based approach alongside grammar-based testing to enhance machine learning fairness. By integrating domain-specific knowledge graphs into the pipeline, biases can be identified and mitigated during preprocessing, ensuring comprehensive fairness. Index Terms—Machine Learning, Artificial Intelligence, Big Data, Fairness, Knowledge Graphs.