Automated decision-making systems (ADM) may significantly affect our everyday life. They can assist us in a number of tasks when used as a reference, or even substitute humans entirely, and they are as much an opportunity to challenge our decision-making processes as they are a mean of reinforcing pre-existing biases. Because even if algorithms are mostly neutral, the data used to train them could (and usually do) encode social biases. For this reason, one of the main approaches to mitigate bias in such a framework is to work on data quality. To assess how the quality of the data affects the outcome of a classification, we made use of two different set of indices, balance measures and fairness measures, relating to different stages of a machine learning pipeline.