The pervasive adoption of Artificial Intelligence (AI) models in the modern
information society, requires counterbalancing the growing decision power
demanded to AI models with risk assessment methodologies. In this paper, we
consider the risk of discriminatory decisions and review approaches for
discovering discrimination and for designing fair AI models. We highlight the
tight relations between discrimination discovery and explainable AI, with the
latter being a more general approach for understanding the behavior of black
boxes
