The central question of this paper is how to enhance supervised learning algorithms with fairness requirement ensuring that any sensitive input does not "`unfairly"' influence the outcome of the learning algorithm. To attain this objective we proceed by three steps. First after introducing...

We are interested in the analysis of the concept of interpretability associated with a ML algorithm. We distinguish between the “How”, i.e., how a black box or a very complex algorithm works, and the “Why”, i.e. why an algorithm produces such a result. These questions appeal to many...

Libra was presented as a cryptocurrency on June 18, 2019 by Facebook. On the same day, Facebook announced plans for Calibra, a subsidiary in charge of the development of an electronic wallet and financial services. In view of the primary risk of sovereignty posed by the creation of Libra, the...

An extensive list of risks relative to big data frameworks and their use through models of artificial intelligence is provided along with measurements and implementable solutions. Bias, interpretability and ethics are studied in depth, with several interpretations from the point of view of...

The study of dependence between random variables is the core of theoretical and applied statistics. Static and dynamic copula models are useful for describing the dependence structure, which is fully encrypted in the copula probability density function. However, these models are not always able...