Scuola Normale Superiore is an institution devoted to research and higher education. It was founded in 1810 by Napoleon decree as a branch of the École Normale Supérieure of Paris. It operates a highly selective student admission procedure. Students are provided with full tuition, college life and international curriculum. It has roughly 300 undergraduate students, 200 PhD students and 110 professors and researchers. It is organized in three areas, the Faculty of Arts, the Faculty of Social Sciences, and the Faculty of Sciences. The last one includes Mathematics, Physics, Biology, Chemistry, and Information Science. SNS has a strong international vocation both at the educational and at the research level. It frequently organises international workshops and summer school with top level speakers and an internationally very broad audience. The SNS researchers involved in the RI have strong competences in the area of complex networks, statistical modelling, machine learning, artificial intelligence, data science, mathematical and computational methods for economics and finance, and econo-physics. The research groups mostly involved in the current project are those in Quantitative Finance, Numerical Analysis and Scientific Computing, and Computer Science. The Quantitative Finance group has a consolidated experience in the microstructure of financial markets and their implications for price formation, identification of market manipulation, and systemic risk. Recently, in collaboration with ETH in Zurich, the group has investigated the dynamics of cryptocurrency markets and the propagation of instabilities in such markets, The group has also a strong experience in the development of innovative models to analyse complex networks, focusing in particular to the identification of suitable centrality metrics and to the study of the dynamics of and on networks. Combining techniques from statistical physics, econometrics, and machine learning, the group has investigated several networked systems including B2B payment systems, interbank networks, social contact networks, neuronal networks in order to 1) forecast the future state of the network from its history 2) characterise the systemic resilience toward shocks and failures (systemic risk). Apart from publication on major scientific journals, the group has also made available the code for such analyses on Github and in the SoBigData repository. SNS has a strong vocation toward high level educational and training activities, hosting several PhD programs, the most relevant for the project is the one in Computational Methods and Mathematical Methods for Science and Finance. SNS also contributes to the support of high level training initiatives such as the Postgraduate Master in Big Data Analytics & Social Mining (https://masterbigdata.it/), the National PhD Artificial Intelligence (https://phdai-society.di.unipi.it/en/training/), and the PhD in Data Science (https://datasciencephd.eu/). The Quantitative Finance group at SNS has also a consolidated activity of research in collaboration with major industry players (e.g. Unicredit, HSBC, A2A, etc.) and public regulators (Banca d’Italia, Consob). Finally, the ERC project XAI: science and technology for explainable decision making, P.I. Fosca Giannotti adds in the flavor of explainability to the usage of AI. The goal is to experiment with the variety of XAI techniques for learning tasks applied to time series datasets. This is of particular interest within the banking area and collaborations with Banca Intesa are aimed at setting up such experiments. Such techniques have been made available through the SoBigData platform and any further experiment contributes to understanding and formalizing the appropriate development pipeline of an AI based system.