Mediation in civil trials can effectively resolve disputes outside of court proceedings, easing the burden on the courts if successful. Efficiency in identifying disputes is essential, as a failed attempt at mediation can lengthen the duration of the trial. The decision rests with the judge/tribunal on the basis of numerous documents that contain certain statements significant to the decision. This paper describes an artificial intelligence, AI, solution to provide a decision support system that can process documents and (i) produce reliable suggestions, (ii) produce substantiated reasons by highlighting the statements that led to the suggestion, and (iii) respect privacy and data security. Explainable AI techniques (XAI) technologies were used for this purpose, resulting in a solution that meets the defined objectives. The solution was developed as part of the research project "Agile Justice," funded in the Italian National Governance and Institutional Capacity NOP, and validated against real cases. The solution leveraged the Snap4City framework for data management and AI/XAI solution.
University of Florence, Italy - ORCID: 0000-0003-1044-3107
Chapter Title
Valutazione della propensione alla mediazione tramite eXplainable AI
Authors
Paolo Nesi
Language
Italian
DOI
10.36253/979-12-215-0316-6.13
Peer Reviewed
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Book Title
Giustizia sostenibile
Book Subtitle
Sfide organizzative e tecnologiche per una nuova professionalità
Editors
Paola Lucarelli
Peer Reviewed
Number of Pages
270
Publication Year
2023
Copyright Information
© 2023 Author(s)
Content License
Metadata License
Publisher Name
Firenze University Press
DOI
10.36253/979-12-215-0316-6
ISBN Print
979-12-215-0315-9
eISBN (pdf)
979-12-215-0316-6
Series Title
Studi e saggi
Series ISSN
2704-6478
Series E-ISSN
2704-5919