Deze bijdrage bevat de tekst van de auteur van de op 18 juni 2021 uitgesproken afscheidsrede als hoogleraar aan de Radboud Universiteit met de leeropdracht sociaal recht. De indertijd uitgesproken versie is voorzien van een notenapparaat.

This article investigates if and in what manner international human rights treaties provide a mandate for state parties to combat hateful homophobic and transphobic speech. It is concluded that under the ICCPR, the path to inclusive readings of its incitement provision is principally paved by state parties. The UN Human Rights Committee welcomes states’ going beyond the codified (Art. 20) prohibition grounds of ‘national, racial or religious hatred’, thus including such grounds as sexual orientation and gender identity. Within the regional context of the ECHR, it is the European Court of Human Rights that functions as a catalyst. In recent jurisprudence, the latter court has developed a positive duty to investigate instances of hateful homophobic speech. Under both regimes a similarly steadfast approach to combating transphobic speech cannot yet be detected.

Previous research has shown that gruesome evidence influences guilt decisions. Several scholars have put forward that the emotional response to gruesome evidence drives this effect. The current systematic review aims to critically scrutinize the current literature on this underlying emotion mechanism, by reviewing studies on the potential mediating role of emotion. A synthesis of the literature suggests that specific emotions have different effects: disgust shows a mediating effect, fear and sadness do not, whereas the effect of anger is inconsistent. The question remains whether an emotional effect is desirable in the courtroom. Recommendations for future research and the legal practice are made.

In recent years, big data technology has revolutionised many domains, including policing. There is a lack of research, however, exploring which applications are used by the police, and the potential benefits of big data analytics for policing. Instead, literature about big data and policing predominantly focuses on predictive policing and its associated risks. The present paper provides new insights into the police’s current use of big data and algorithmic applications. We provide an up-to-date overview of the various applications of big data by the National Police in the Netherlands. We distinguish three areas: uniformed police work, criminal investigation, and intelligence. We then discuss two positive effects of big data and algorithmic applications for the police organization: accelerated learning and the formation of a single police organization.

This article starts with a brief exploration of the juridical and criminological meaning of money laundering. Subsequently, the author discusses two contributions that gained momentum in the Dutch debate on money laundering. This concerns, firstly, the statements of Roberto Saviano, an Italian journalist who claims that the Dutch tax regulations facilitate organized crime. Secondly, the author discusses Unger’s application of the so-called Walker model to estimate the size of money laundering in the Netherlands (and worldwide). Finally, he argues that, due to the limited, current level of empirical knowledge on criminal money flows, a question such as ‘How much money is laundered in the Netherlands?’, is in fact not suited as a research question.

The promises of big data have reached the domain of safety and security. After these techniques proved their added value in the private sector, they soon reached the attention of the public sector. In this article we present the results of our interviews with 27 executive professionals of the Dutch police and the department of public order and safety of local governments. What are their expectations of big data? Their experience with big data varied. Our respondents gave expression to a basic positive attitude towards intelligence led policing and the chances that big data brings to it. But anticipation of working with big data was not free from discussion and the respondents stressed the need for securing privacy and other constitutional rights of citizens before big data applications are implemented in the police function. With that, our respondents strongly aligned with the international, academic call for a constitutional reflection about the application of big data withing the police function.

The availability and use of big data sources is increasing exponentially. The variety of new and emerging data sources offers opportunities to complement, replace, improve or add to conventional data sources. Survey data are one kind of conventional data sources. In survey research, a framework to assess the accuracy of survey data already existed for quite some time. This framework is known as the Total Survey Error (TSE) framework. The philosophy behind this framework has only recently been universalized to (big) data in general in the form of the Total Error (TE) framework. This generic framework, which allows for assessing the accuracy of (big) data, is outlined in this article. Additionally, the TE framework is applied to big data sources that could be relevant for policing: police-registered crime data, Twitter data and mobile phone data.