While still debating some questions (such as the likelihood of achieving Artificial General Intelligence), the AI community in particular and most of the related stakeholders in general, seem to be more or less convinced that "AI winters" are a thing of the past and that the current "summer" will never end. Indeed, this rapidly evolving field, especially through the recent Deep Learning advances, is too good at particular useful tasks to simply disappear. There is little doubt, for example, that neural networks are poised to permeate a growing number of everyday applications, including sensitive software where trust is paramount.

But as these artifacts move from a fad status to more stably ubiquitous components, their deepening interweaving with different aspects of society deserves a special attention to the development of methods and tools for an adequate characterization of AI trustworthiness. This colossal quest is made difficult by the intrinsic opacity of neural networks and their increasing size, making any method that can bring us closer to trustworthiness a precious commodity. In this talk, we take a retrospective look at some of these methods, and discuss their current added value to safety, as well as the promises they hold for the future of AI trustworthiness.