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Date Trend snippet:
23 June 2021
Relevance date:
2020
Type of Treath or Oppertunity
Domain of application
Source of threat
Victim
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- Trend snippet: Growing deployment of predictive policing tools around the world, even while the empirical evidence is inconclusive
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The HSD Trendmonitor is designed to provide access to relevant content on various subjects in the safety and security domain, to identify relevant developments and to connect knowledge and organisations. The safety and security domain encompasses a vast number of subjects. Four relevant taxonomies (type of threat or opportunity, victim, source of threat and domain of application) have been constructed in order to visualize all of these subjects. The taxonomies and related category descriptions have been carefully composed according to other taxonomies, European and international standards and our own expertise.
In order to identify safety and security related trends, relevant reports and HSD news articles are continuously scanned, analysed and classified by hand according to the four taxonomies. This results in a wide array of observations, which we call ‘Trend Snippets’. Multiple Trend Snippets combined can provide insights into safety and security trends. The size of the circles shows the relative weight of the topic, the filters can be used to further select the most relevant content for you. If you have an addition, question or remark, drop us a line at info@securitydelta.nl.
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Growing deployment of predictive policing tools around the world, even while the empirical evidence is inconclusive
Despite the growing deployment of predictive policing tools around the world,33 the empirical evidence is yet to conclusively validate the claims of consistent and significant reductions in crime. Similarly, serious concerns are frequently raised about the potentially negative impact on human rights, police accountability, and the fairness of law enforcement interventions.
More source info
IIn general terms, the concept of predictive policing refers to the use of analytical techniques to assess a broad variety of data in order to anticipate potential occurrences of crime.26 By using statistical models, predictive policing tools can identify the locations, persons and circumstances most likely to be involved in criminal acts.27 This information can be used to guide police interventions and support the ability of law enforcement agencies to assess crime data for the purpose of acting in a proactive and targeted manner. To this end, predictive policing tools generally fall into two main categories. Predictive mapping or placeoriented techniques aim to anticipate the locations in which crime is most likely to take place at a given time and under certain circumstances.28 These techniques can identify so-called ‘crime hotspots’ in order to effectively deploy police resources by analysing police records, crime trends and other variables determined to be relevant to the occurrence of criminal acts. Similarly, predictive identification or person-oriented applications seek to determine which individuals are at higher odds of committing crimes or being victimized thereby.29 By conducting risk assessments and establishing the circumstances that increase the likelihood of a person being involved in a crime, law enforcement agencies can identify high-risk individuals so as to intervene proactively and steer them away from crimes. Beyond the use of these techniques by law enforcement agencies and public authorities, predictive policing may also entail the involvement of private entities. A part of the fight against financial crime, money laundering and terrorist financing, for instance, is being outsourced to banks, which have to abide by their EU Anti-Money Laundering (AML) obligations.30 In trying to comply with the latter, banking institutions often resort to predictive analytics on transactional activities in order to identify potential future criminal behaviour and, if possible, prevent the manifestation of crime.31 However, while predictive analytics employed by law enforcement authorities feed on mostly criminal data, predictive analytics performed by banking institutions rely primarily on data generated in the course of regular commercial and financial transactions. Still, the underlying logic remains the same, and the algorithms employed must be trained to identify potentially criminal intentions behind transactions. Furthermore, while the technologies behind predictive policing carry great promise and are often presented as a revolution in the fight against crime32, the effectiveness and impact of the practice remain contested and controversial. Despite the growing deployment of predictive policing tools around the world,33 the empirical evidence is yet to conclusively validate the claims of consistent and significant reductions in crime.34 Similarly, serious concerns are frequently raised about the potentially negative impact on human rights, police accountability, and the fairness of law enforcement interventions.35 It is in the context of this controversy and uncertainty that this article aims to assess the risks of function creep in predictive policing tools that can exacerbate these issues and might necessitate a different approach to purpose limitation to improve the accountability and reliability of these applications.
