In today’s digital age, algorithmic decision-making systems play a crucial role in fields like credit scoring and medical diagnoses. While they are often praised for being ‘objective,’ these systems can exhibit biases, mostly stemming from the data they rely on. Current methods for reducing bias in machine learning tend to focus on fixing the surface-level issues, rather than addressing the root causes. Taking inspiration from the idea that “garbage in, garbage out,” it becomes clear that we need to shift towards a data-centric approach. This means improving data quality right from the beginning. Our research aims to use established data quality management practices from the database field to effectively tackle the underlying causes of algorithmic biases, resulting in more dependable and fair algorithmic systems.