As many specific studies proved, biased data and lack of fairness evaluation criteria may typically lead algorithmic tools to discriminatory behaviour against vulnerable minorities and protected groups of our society. The transposition of philosophical concepts, like ethics and fairness, into our data-driven world is an extremely challenging operation at least for two fundamental reasons: 1. The attempts of formalization of such concepts contain the same echoes of these historic philosophical debates. 2. These concepts are relative to socio-cultural factors, and they are widely variable depending on the field of application, the country, the government, the religion, the epoch, etc. While the law is still failing in regulating the usage of algorithmic tools in the most delicate processes, data scientist community is focusing more and more on ethics, proposing different methods and different approaches.