Time anomaly detection in the duration of civil trials in Italian justice

Abstract

Through the digitalisation of Civil Trials and the implementation of the Telematic Civil Process framework, the Italian Ministry of Justice has amassed a wealth of data covering all facets of modern Trials. While data availability has surged, the focus now lies in actively analysing this data to optimise Trials and curtail their duration. This paper, with a strong emphasis on its outcomes, delves into the analysis of data from the Court of Livorno. It seeks to pinpoint specific events within the Trial workflow that significantly extend Trial durations. Notably, Domain Experts have identified a set of events, intending to validate their pivotal role in recognising critical Trials. Leveraging Machine Learning techniques, the paper evaluates multiple binary classifiers to proactively identify potentially critical Trials, empowering Judges to mitigate future issues. The study has yielded a remarkable 80% accuracy rate in predicting Trials exceeding acceptable duration thresholds.

Keywords:

• Civil trials
• telematic civil process
• machine learning
• binary classification
• trial duration

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work described in this paper has been supported by the Project VALERE “SSCeGov - Semantic, Secure and Law Compliant e-Government Processes”; Universitá degli Studi della Campania Luigi Vanvitelli [Valere - SSCeGov].