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Trends in Security Information
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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Algorithms allow for high-speed and automated decision making on a very large scale
1.4.5 The scale and speed challenge As explained in section 1.3, algorithmic decision making can be used to allow for high-speed, sometimes automated decision making on a very large scale.140 This can certainly help to enable extensive decision making in the public sector, for example in relation to traffic fines, routine social security and taxation decisions or the granting of permits. In the private sector, algorithms are also increasingly used to automatise decision making. Well-known examples are price-setting by web shops based on individual preferences and buying behaviour, making individual offers by platforms such as Uber or Airbnb, or individual targeting by video platforms or newsfeeds. Different types of algorithms can be used to effectuate such volumes and speed. Sometimes rule-based algorithms work best, while machine-learning or deep-learning algorithms are better at generating the desired effects in other cases. Nevertheless, what all algorithms have in common is that they allow for decision making on a much larger scale than traditional human decision making is capable of, and with unprecedented speed.141 This innovation in the scale and speed of decision making poses a new and general challenge from the perspective of non-discrimination. This is even more true if we take account of the characteristics and challenges discussed above.142 Flaws in human thinking, the existence of structural forms of societal discrimination and stereotyping, and the lack of representative and accurate data may negatively influence the process of designing, developing and using an algorithm. If insufficient checks are made, the particular characteristics of algorithmic decision making can cause algorithmic discrimination to ‘spread’ at a wider scale and a much quicker pace than ‘human’ discrimination could do.