In the last ten years, advances in the field of Artificial Intelligence have been impressive with many achievements such as the defeat of the best Go players against AlphaGo, the AI-based computer program. To solve these difficult problems, the resolution algorithms are becoming more and more sophisticated and complex: therefore, the interpretability of Deep Learning models is difficult. Moreover, that complexity can be an obstacle to the use of deep learning algorithms (business and operational users will not understand the algorithm and, at the end, will not adhere to the methodology) and easily lead to biases and even ethical problems (e.g. The notion of interpretability is thus important: by using specific models or interpretability methods, it is possible to make the results but also the problem much more understandable and easily explainable for human beings. Potential biases are more easily detectable and avoidable.