Explainable Artificial Intelligence (XAI) is an emergent research field which tries to cope with the lack of transparency of AI systems, by providing human understandable explanations for the underlying Machine Learning models. This work presents a new explanation extraction method called LEAFAGE. Explanations are provided both in terms of feature importance and of similar classification examples. The latter is a well known strategy for problem solving and justification in social science. LEAFAGE leverages on the fact that the reasoning behind a single decision/prediction for a single data point is generally simpler to understand than the complete model; it produces explanations by generating simpler yet locally accurate approximations of the original model. LEAFAGE performs overall better than the current state of the art in terms of fidelity of the model approximation, in particular when Machine Learning models with non-linear decision boundaries are analysed. LEAFAGE was also tested in terms of usefulness for the user, an aspect still largely overlooked in the scientific literature. Results show interesting and partly counter-intuitive findings, such as the fact that providing no explanation is sometimes better than providing certain kinds of explanation.

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Product customisation is a topic of growing interest in Smart Manufacturing. Allowing customers to design intended products brings additional challenges to the manufacturing task, such as the increase in flexibility of the assembly theatre, the compilation of assembly instructions for possibly unique products, and stress-related risks for human operators. This work introduces ViTroVo, an artificial intelligence framework capable of (1) autonomously building a graph of assembly steps via trial-and-error (in vitro Assembly Search) and (2) presenting relevant instructions to a human operator and, by autonomously detecting her progress and affective state, adapting accordingly (in vivo Adaptive Operator Guidance). The power of ViTroVo resides in its versatile way to manipulate a given product’s component Augmented Computer Aided Design (CAD+) models throughout the whole assembly task. We conducted an empirical evaluation involving participants instructed to assemble a previously unseen product. The encouraging results make us believe ViTroVo’s architecture could become the foundations of highly customised flexible manufacturing.

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