The opaque nature of machine learning systems has raised concerns about whether these systems can guarantee fairness. In addition, ensuring fair decision-making requires that multiple perspectives of fairness be considered. Currently, there is no agreement on the definitions, the facilitation of shared interpretation is difficult, and there is a lack of a unified formal language to describe them. Current definitions are implicit in the operationalization of systems, making them difficult to compare. In this thesis, we discuss how to make fairness actionable, providing concrete tools for that. We provide not only conceptual elements to model and abstract problems of fairness, but also a technical framework and a description language.