There is an increasing amount of work from different communities in data mining, machine learning, information retrieval, semantic web, and databases on bias discovery and discrimination-aware learning with the goal of developing not only good quality models but also models that account for fairness. In this work, we focus on supervised learning where biases towards certain attributes like race or gender might exist. We propose data augmentation techniques to correct for bias at the input/data layer. Our experiments with real world datasets show the potential of augmentation techniques for dealing with bias.