Investigating sentence severity with judicial open data -- A case study on sentencing high-tech crime in the Dutch criminal justice system

TL;DR

This study analyzes Dutch court judgments to explore how anonymized open data can be used for criminological research, revealing that offenders using advanced ICT face longer sentences and that open data research quality is comparable to traditional databases.

Contribution

It demonstrates the viability of using anonymized judicial open data for sentencing research, specifically linking ICT use to sentence severity in a case study.

Findings

Offenders using advanced ICT receive longer custodial sentences.

Open data research quality matches that of traditional judicial databases.

Analysis of 25,366 court judgments from 2015-2020.

Abstract

Open data promotes transparency and accountability as everyone can analyse it. Law enforcement and the judiciary are increasingly making data available, to increase trust and confidence in the criminal justice system. Due to privacy legislation, judicial open data -- like court judgments -- in Europe is usually anonymised. Because this removes part of the information on for instance offenders, the question arises to what extent criminological research into sentencing can make use of anonymised open data. We answer this question based on a case study in which we use the open data of the Dutch criminal justice system that rechtspraak.nl/Uitspraken makes available. Over the period 2015-2020, we analysed sentencing in 25,366 court judgments and, in particular, investigated the relationship between sentence severity and the offender's use of advanced ICT -- as this is information that is readily available in open data. The most important results are, firstly, that offenders who use advanced ICT are sentenced to longer custodial sentences compared to other offenders. Second, our results show that the quality of sentencing research with open data is comparable to the quality of sentencing research with judicial databases, which are not anonymised.