The goal of the FATE system is decision support with use of state-of-the-art human-AI co-learning, explainable AI and fair, secure and privacy-preserving usage of data. This AI-based support system is a general system, in which the modules can be tuned to specific use cases. The FATE system is designed to address different user roles, such as a researcher, domain expert/consultant and subject/patient, each with their own requirements. Having examined a Diabetes Type 2 use case before, in this paper we slightly iterate the FATE system and focus on a juridical use case. For a given new juridical case the relevant older court cases are suggested by the system. The relevant older cases can be explained using the eXplainable AI (XAI) module, and the system can be improved based on feedback about the relevant cases using the Co-learning module through interaction with a user. In the Bias module, the use of the system is investigated for potential bias by inspecting the properties of suggested cases. Secure Learning offers privacy-by-design alternatives for functionality found in the aforementioned modules. These results show how the generic FATE system can be implemented in a number of real-world use cases. In future work we plan to explore more use cases within this system.

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.