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Veiligheid uit de glazen bol?

Naar verantwoorde toepassingen van big data in het veiligheidscomplex

Tijdschrift Tijdschrift voor Veiligheid, Aflevering 3-4 2019
Trefwoorden Big data, Security, good governance
Auteurs Remco Spithoven en Siri Beerends
SamenvattingAuteursinformatie

    The promises of Big Data, predictive policing and artificial intelligence hold a key position in the public debate for quite some time now. Optimists tell that it is possible to predict where criminal events will occur before they take place. This would implicate a major shift towards a crime and insecurity preventive society, feeding on our cultural longing for a secure future. Therefore we give algorithms and deep learning access to more and more aspects of our lives. But how realistic and desirable is the application of Big Data techniques in the area of security?
    In this article we put focus on the research question ‘In which way can Big Data and predictive policing support good governance of security?’, that has led our study. By exploring the central concepts, the processes behind them and their results in the domain of public security, we conclude that there are only rather disappointing results from the application of these techniques: crime and insecurity have not dropped when the police and other organizations turned to Big Data techniques. Instead, many negative side effects occurred. We search for explanations in six central academic critiques on the application of these techniques in the area of security.
    We have found several ways to guaranty principles of good governance in the application of Big Data techniques, but these require a firm paradigm shift on Big Data in general. The heuristics of security professionals should not be overshadowed by technological promises: the professional should always be in the loop, must understand the way predictions come into existence and must be able to correct flaws and bugs of (semi-)automated decisions. We conclude that safeguarding public security must remain human work in which Big Data techniques can assist.


Remco Spithoven
Remco Spithoven is lector Maatschappelijke Veiligheid aan de Hogeschool Saxion en redacteur van dit tijdschrift.

Siri Beerends
Siri Beerends is cultureel socioloog, onderzoeker en schrijver bij SETUP en pro‍movenda aan de Universiteit Twente.

    The economic analysis of (potential) disasters is an important method to determine the efficacy and efficiency of investments in disaster prevention and mitigation. The Dutch National Risk Assessment (NRA) provides an integrated, whole-of-government and all-hazard approach to Dutch national security. The strategy does not only intend to identify capacity gaps and define measures regarding individual threats and risks, but also to enhance capability planning and policy development concerning overall national security. The approach is multi-disciplinary and based upon scenarios which are evaluated and graded in terms of impact and likelihood according to a unified scoring method. Economic impact is one of the criteria in the NRA risk assessment methodology. This article provides a review of the (applied) scientific literature of the many economic tools and methods that have been used worldwide to estimate the (potential) impact of disasters and provides concrete applications at the micro and macro levels to Dutch cases and scenarios that were developed during the five annual cycles of the NRA's existence (2007-2011). We discuss pros and cons of applied methodologies.


Peter van Bergeijk
Peter van Bergeijk is hoogleraar Internationale economie en Macro-economie aan het International Institute of Social Studies van de Erasmus Universiteit.

Marcel Mennen
Marcel Mennen is algemeen secretaris van het Analistennetwerk Nationale Veiligheid en senior onderzoeker CBRN aan het Rijksinstituut voor Volksgezondheid en Milieu, Centrum voor Veiligheid te Bilthoven.
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