Time anomaly detection in the duration of civil trials in Italian justice - Antonio Esposito; Beniamino Di Martino; Rosario Ammendolia; Pietro Lupi; Massimo Orlando; Wei Liang (Connection Science)
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.