With an ever-growing world population, mass incidents like those at the Astroworld Festival in 2021 and Halloween in Seoul in 2022 may occur more often. Crowd management, with artificial intelligence (AI) as a promising tool, can offer solutions. AI can analyze crowds, predict escalations, and enable early interventions. Detecting group emotion plays a crucial role, which can be achieved through various data modalities, each with its own advantages and disadvantages. Multi-agent simulation models then predict emotional contagion effects in crowds. Various considerations are discussed, with privacy, validity, and applicability being central.

In the criminal justice process and during sentencing, there are several moments when crucial advice needs to be provided. Because these moments have significant consequences for those involved and society, it is essential that these processes are executed with care, and continuous efforts are made to improve and ensure the quality of these processes. With the rise of AI, the question has emerged of whether this technology can assist in enhancing considerations in the context of forensic diagnostics. On the one hand, there is consideration of possibilities for automation, allowing human intervention to be partially removed from the process. On the other hand, there is a consideration of strengthening human capabilities by deploying them more efficiently or effectively. This article delves into the experiences gained from research on how AI can contribute to complex decisions in forensic diagnostics, particularly in the assessment for clinical forensic care by the Netherlands Institute of Forensic Psychiatry and psychology (NIFP).

The security domain is currently facing major challenges, both internationally and nationally. The Royal Netherlands Marechaussee is therefore committed to the development of artificial intelligence (AI) to improve its effectiveness. This article describes three possible applications of AI within the Marechaussee: the virtual border guard, smart analysis of sensor data, and autonomous robotics for security tasks. The article then discusses a number of potential problems related to the use of AI: the effectiveness, bias and transparency of algorithms, and the lack of a legal framework. Examples and mitigating measures are given for each of the potential problems.

Artificial intelligence (AI) is slowly but fundamentally changing police practice. Processes of human sensemaking are increasingly augmented and sometimes automated by AI. As a consequence, policemen have to work together with police machines. This requires new knowledge and skills. The foundation of these competences is a digital mindset. In addition, data skills, software skills, and legal and ethical knowledge and skills are crucial parts of the required craftmanship. Police organizations are faced with the task of facilitating learning processes. These processes preferably take place in operational teams by supporting the use of AI applications for tasks at hand.

In dit artikel wordt nagegaan of de ambient population, oftewel het aantal mensen dat op een bepaald tijdstip in een bepaald gebied aanwezig is, een geschiktere maatstaf biedt om het criminaliteitsrisico in tijd en ruimte in te schatten voor delicten met mobiele doelwitten dan de residentiële populatie. Meer specifiek wordt de potentie van het gebruik van deze ambient population als noemer in de berekening van criminaliteitsratio’s en voorspeller van criminaliteitsrisico’s voor predictieve modellen geëvalueerd. Hierbij wordt gebruikgemaakt van mobiele-telefoondata voor een periode van drie maanden in Gent (n = 595.858.852 ruwe datapunten). Uit de resultaten blijkt dat de ambient population sterker samenhangt met geregistreerde criminaliteit dan de residentiële populatie. Bovendien wijzen criminaliteitsratio’s op basis van de ambient population ook andere probleemgebieden aan dan die op basis van de residentiële populatie. De voorspellingskracht van predictieve modellen voor tijdruimtelijke spreiding van criminaliteit kan worden verbeterd door gebruik te maken van de ambient population. Deze resultaten bevestigen dat de ambient population een geschiktere maatstaf biedt voor delicten met een mobiel doelwit, omdat deze de onderliggende tijdruimtelijke dynamiek beter reflecteert.