AI Applied to the Analysis of the Contracts of the Italian Public Administrations

The proliferation of e-procurement systems in the public sector allows for joint access to useful and open information sources. Our research explores ways to improve the quality and correctness of the public procurement process and the efficiency of administrations, the reduction of the time spent by economic operators, and the costs of public administrations. In particular, we explored the dataset of the National Anti-Corruption Authority in Italy on public procurement and the judges' sentences related to public procurement. Our first goal was to identify which procurement led to disputes and recourse to Administrative Justice by identifying relevant procurement features. Our second goal was to develop a recommender system on procurement by applying machine learning algorithms and deep neural models to return similar procurement to a given one and find companies as potential bidders, depending on the procurement requirements. Our third goal is to automate the analysis of a dataset of public procurement, contract awards, and appeal procedures. Process discovery techniques were applied to the dataset, considering control-flow, organizational (resource), and time perspectives. The results demonstrate the importance of applying these techniques in the legal field.