This thesis tackles the issue of bias in Artificial Intelligence (AI) systems, specifically focusing on the mitigation of gender and racial biases through a data-centric approach that seeks to achieve more equitable results. As AI systems become more prevalent in critical sectors such as healthcare, finance, and criminal justice, they risk unintentionally reinforcing and amplifying societal biases that are present in the data they learn from. To address this, this research explores two main techniques for bias mitigation: preprocessing bias correction methods using the AI Fairness 360 (AIF360) toolkit and synthetic data generation with ClearboxAI's Tabular Engine to augment underrepresented groups.