Addressing the problem of fairness is crucial to safely use machine learning algorithms to support decisions with a critical impact on people's lives such as job hiring, child maltreatment, disease diagnosis, loan granting, etc. Several notions of fairness have been defined and examined in the past decade, such as statistical parity and equalized odds. The most recent fairness notions, however, are causal-based and reflect the now widely accepted idea that using causality is necessary to appropriately address the problem of fairness. The main objective of our research is to measure discrimination as accurately as possible. To this end, we make a distinction between the concepts of "bias" (a deviation of an estimation from the quantity it estimates) and "discrimination" (the unjust or prejudicial treatment of different categories of people on the ground of race, age, gender, disability, etc.). In this seminar we illustrate why causality is essential to reach this objective of accurately measuring fairness.