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- Trend snippet: The potential impact of deep-fake manipulation and fabrication of audiovisual material offered as evidence in the criminal justice system
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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The potential impact of deep-fake manipulation and fabrication of audiovisual material offered as evidence in the criminal justice system
We have suggested that there is a two-stage challenge posed by GAN-facilitated deep-fakes to criminal justice systems. In the first stage, when treated in isolation, deep-fakes pose a limited problem concerning their evidential veracity that can be likely accommodated by a robust judicial system. In the second stage, however, the combinatorial nature of the affordances presented by deep-fakes combines within the ubiquity context of audiovisual recording and replication to undermine implicit, yet foundational, epistemic presumptions. If taken seriously, such erosions threaten the very possibility of relying on broad categories of evidence in criminal procedure. Such an analysis is made possible not by examining the intrinsic nature and characteristics of artificial intelligence, nor by dissecting its particular applications, but rather by situating the effects within the conceptual framework of affordances. Yet, even such an examination is insufficient to truly grasp future challenges and it will only be through the more detailed analysis using the combinatorial approach that we will even begin to engage with the impending fundamental shifts in the sociotechnical landscape. This exercise hints at the oblique nature of the challenges posed by AI for criminal justice that cannot be identified in isolation beforehand, and is a powerful assertion of the Collingridge dilemma.48 Our suggestion for attempts to frame criminal justice responses to AI challenges goes beyond the conceptual framework of affordances proposed here (which is but one possible approach) and suggests we look to how the deployment of the technology combines both with the existing sociotechnical landscape, and with the recognised and potential uses of other new and emerging technologies. It is likely that, although AI will pose deep challenges in isolation, the more fundamental questions will lie at the Lagrangian points between new technologies.