Susan W. van den Braak

Combining and Analyzing Judicial Databases

To monitor crime and law enforcement, databases of several organizations, covering different parts of the criminal justice system, have to be integrated. Combined data from different organizations may then be analyzed, for instance, to investigate how specific groups of suspects move through the system. Such insight is useful for several reasons, for example, to define an effective and coherent safety policy. To integrate or relate judicial data two approaches are currently employed: a data warehouse and a dataspace approach. The former is useful for applications that require combined data on an individual level. The latter is suitable for data with a higher level of aggregation. However, developing applications that exploit combined judicial data is not without risk. One important issue while handling such data is the protection of the privacy of individuals. Therefore, several precautions have to be taken in the data integration process: use aggregate data, follow the Dutch Personal Data Protection Act, and filter out privacy-sensitive results. Another issue is that judicial data is essentially different from data in exact or technical sciences. Therefore, data mining should be used with caution, in particular to avoid incorrect conclusions and to prevent discrimination and stigmatization of certain groups of individuals.

Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector

For public organizations data integration and sharing are important in delivering better services. However, when sensitive data are integrated and shared, privacy protection and information security become key issues. This means that information systems must be secured and that access to sensitive data must be controlled. In this paper, a framework is presented to support data sharing between public organizations for collaboration purposes. The framework focuses on solutions towards optimal data sharing and integration while ensuring the security and privacy of individuals. Data sharing is based on the need-to-know principle, that is, data are only made available when they are required to perform core processes. To facilitate this, an approach is introduced in the form of a trusted third party that manages access control to personal information and thus helps to protect the privacy of individuals. It is argued that the proposed framework is suitable for data integration and sharing on various levels. An example of best practices of data sharing in the Netherlands shows how this framework facilitates data sharing to perform knowledge transfer and other higher-level tasks.

Linking open data: challenges and solutions

Open data have been recognized as a way to advance citizen engagement, increase economic welfare and improve policy-making and public decision-making. The linking of all kinds of open data sets provides potentially many benefits. Yet the number of successful examples of combining datasets has been limited. Challenges can be found at the political, social, use, and technical level. In this half day workshop we discuss the challenges of open data and relate these to the needs for policies and infrastructures for processing data. Various ways of dealing with these challenges will be discussed and in particular we will present an infrastructure for processing open data. This infrastructure harnesses open data sources and methodologies for annotating, visualising and making open data available to scientists and citizens. Participants are asked to actively participate in discussion and are invited to provide feedback and suggestions for improvement.

A Big Data Approach to Support Information Distribution in Crisis Response

Crisis response organizations operate in very dynamic environments, in which it is essential for responders to acquire all information critical to their task execution in time. In reality, the responders are often faced with information overload, incomplete information, or a combination of both. This hampers their decision-making process, workflow, situational awareness and, consequently, effective execution of collaborative crisis response. Therefore, getting the right information to the right person at the right time is of crucial importance. The task of processing all data during crisis response situations and determining for whom at a particular moment the information is relevant is not straightforward. When developing an information system to support this task, some important challenges have to be taken into account. These challenges relate to the structure and truthfulness of the used data, the assessment of information relevance, and the dissemination of relevant information in time. While methods and techniques from big data can be used to collect and integrate data, machine learning can be used to build a model for relevance assessments. An example implementation of such a framework of big data is the TAID software system that collects and integrates data communicated between first responders and may send information to crisis responders that were not addressed in the initial communication. As an example of the impact of TAID on crisis response, we show its effect in a simulated crisis response scenario.

On Enabling Smart Government: A Legal Logistics Framework for Future Criminal Justice Systems

While in business and private settings the disruptive impact of advanced information communication technology (ICT) have already been felt, the legal sector is now starting to face great disruptions due to such ICTs. Bits and pieces of innovations in the legal sector have been emerging for some time, affecting the performance of core functions and the legitimacy of public institutions. In this paper, we present our framework for enabling the smart government vision, particularly for the case of criminal justice systems, by unifying different isolated ICT-based solutions. Our framework, coined as Legal Logistics, supports the well-functioning of a legal system in order to streamline the innovations in these legal systems. The framework targets the exploitation of all relevant data generated by the ICT-based solutions. As will be illustrated for the Dutch criminal justice system, the framework may be used to integrate different ICT-based innovations and to gain insights about the well-functioning of the system. Furthermore, Legal Logistics can be regarded as a roadmap towards a smart and open justice.

Sharing confidential data for algorithm development by multiple imputation

The availability of real-life data sets is of crucial importance for algorithm and application development, as these often require insight into the specific properties of the data. Often, however, such data are not released because of their proprietary and confidential nature. We propose to solve this problem using the statistical technique of multiple imputation, which is used as a powerful method for generating realistic synthetic data sets. Additionally, it is shown how the generated records can be combined into networked data using clustering techniques.

A method for explaining and predicting trends: an application to the Dutch justice system

A method, named Trendwatch, has been developed for explaining and predicting trends in which a structural break has occurred. Using this method it is possible to explain such a break in terms of causal networks of factors. The thus created explanation is validated using evidential arguments based on expert opinions. Moreover, this explanation is used to predict the future course of the trend after the break. To do so, experts are asked to predict the development of the factors at the beginning of the explanation.

Development and Use of Data-Centric Information Systems to Support Policymakers: Applied to Criminal Justice Systems

Reliable management information is invaluable for policymakers and advisers to make informed policy decisions. Depending on the information needs of the policymakers and the characteristics of the data required, different approaches exploiting different ways to process data from multiple sources, may be used. In this chapter, we describe three information systems currently in use in the Dutch criminal justice system. The first system is based on a dataspace approach and uses aggregate data to provide a view on the current state of the criminal justice system. This is particularly useful for evaluating current policy and monitoring the implementation of new policy. The second system utilizes a data warehouse to integrate individual level data and look back to older cases. Therefore, it is suitable for evaluating policy. Finally, the third system exploits time series data to forecast the capacity needed in the near future. This allows for planning new policy and monitoring its implementation. Based on our experience with developing and implementing such systems and their use in practice, we lay down a list of guidelines for developing management information systems in the public sector. These guidelines also address issues like data quality, misinterpretation, and privacy protection.

Legal Logistics: A Framework to Unify Data Centric Services for Smart and Open Justice

This paper presents a framework to provide a unified view towards the visions of smart and open justice. The framework, coined as Legal Logistics, aims at unifying and embodying different data-centric services that exploit available and relevant data for supporting and enhancing the legitimacy and efficiency of legal systems. As such, the framework specifies the scope of data-centric services in legal systems. Such a unified view of data-centric services, enables the authors to relate these services to each other and to their operational context, and better streamline data-centric based innovations in legal systems. Two data-centric services realized for the Dutch criminal justice system will be discussed. These services innovatively integrate different datasets in order to give some insights about the well-functioning and budgetary needs of the Dutch legal system. These use cases primarily illustrate the typical challenges and benefits of realizing the vision of smart justice. Secondarily, they illustrate the relevancy and usefulness of the embodying Legal Logistics framework.

Fraud Indicators Applied to Legal Entities : An Empirical Ranking Approach

Legal persons (i.e., entities such as corporations, companies, partnerships, firms, associations, and foundations) may commit financial crimes or employ fraudulent activities like money laundering, tax fraud, or bankruptcy fraud. Therefore, in the Netherlands legal persons are automatically screened for misuse based on a set of so called risk indicators. These indicators, which are based on the data obtained from, among others, the Netherlands Chamber of Commerce, the Dutch police, and the Dutch tax authority, encompass information about certain suspicious behaviours and past insolvencies or convictions (criminal records). In order to determine whether there is an increased risk of fraud, we have devised a number of scoring functions to give a legal person a score on each risk indicator based on the registered information about the legal person and its representatives. These individual scores are subsequently combined and weighed into a total risk score that indicates whether a legal person is likely to commit fraud based on all risk indicators. This contribution reports on our two ranking approaches: one based on the empirical probabilities of the indicators and the other based on the information entropy rate of the empirical probabilities.