What Can Machine Learning Do for the Public
Procurement?
Rosa Meo1 , Roberto Nai1 and Paolo Pasteris1
1
    Università degli studi di Torino, Via Verdi 8, 10124 Torino, Italy


                                         Abstract
                                         We present the systematic work we conducted on the data about public procurement in Italy. The goal is
                                         to clean and integrate various public and open information sources and extract valuable information for
                                         the public sector and the companies interested in awarding a contract with the Public Administration.
                                         Included in the data analysis is the Regional Administrative Justice that receives recourses from the
                                         involved actors. This information coming from recourses is potentially useful for revealing some of the
                                         anomalies related to the incorrect behaviour of the partners. The obtained results can also make lighter
                                         the administrative judges’ workload.




1. Bio
Rosa Meo is associate professor in Computer Science at the University of Torino. Her research
area is in the field of Data Mining, Machine Learning and NLP. She is active in the main
conference program committees and journal editorial boards related to Data Mining. The work
presented is the result of an active collaboration with prof. Gabriella Racca (Administrative
Law) and ANAC (Italian National Authority Anti Corruption).




AIxPA 22: 1st Workshop on AI for Public Administration, December 2nd, 2022, Udine, IT
Envelope-Open rosa.meo@unito.it (R. Meo); roberto.nai@unito.it (R. Nai); paolo.pasteris@unito.it (P. Pasteris)
GLOBE https://informatica.unito.it/persone/rosa.meo (R. Meo); http://informatica.unito.it/persone/roberto.nai (R. Nai);
https://www.unito.it/persone/ppasteri (P. Pasteris)
Orcid 0000-0002-0434-4850 (R. Meo)
                                       © 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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                  ISSN 1613-0073
                                       CEUR Workshop Proceedings (CEUR-WS.org)