Looking Ahead Through the Data Jungle. Datafication and Prevention \u2013 An Introduction<\/strong> That governments are interested in data is by no means a new insight. But with advancing digitalization, potential uses for information are reaching new levels \u2013 and so is their pursuit by the police. The already large administrative appetite for data is now falling upon a more susceptible society that is increasingly adhering to the dogma of prevention and is redefining its understanding of security and risk.<\/p>\n From Card Files to Data Warehouses<\/strong> The history of automated data processing by the German police began with the INPOL system in the 70s. Since the 90s, the police have regularly presented new plans to modernize their information systems. INPOL-neu was followed by PIAV, and with the current \u201cPolizei 2020\u201d project, the next attempt is being made at replacing the separate \u201cdata silos\u201d with a joint data warehouse for all of German police.<\/p>\n Tactical Resource: Smartphone<\/strong> Equipping German police with smartphones is part of the \u201cPolizei 2020\u201d project and aims at facilitating police officers\u2019 daily work routines. But smartphones are not merely technical devices influencing official day-to-day routine through apps and messengers. As social objects, they suggest actions and communicative practices which discursively duplicate crisis and hazard scenarios.<\/p>\n Data Protection is Turning into an Empty Vessel<\/strong> Johannes Caspar unsuccessfully tried to bar the police from storing tens of thousands of facial images after the G20 summit. Matthias Monroy talked to him about the role of data protection in the automation of information systems, police in social media, encryption, and EU cooperation.<\/p>\n Artificial Intelligence in Police Work<\/strong> At least 75 countries are employing artificial intelligence (AI) for police purposes. Pilot projects exist in Germany, as well. In general, AI causes investigations to shift to an earlier point in time due to machine-detected correlations. The problems become apparent when considering how machines learn, how they reach decisions, and how these decisions are dealt with.<\/p>\n A Short History of Automated Facial Recognition<\/strong> In a concerted action by police and security authorities on the one hand \u00a0and commercial enterprises on the other, the development of automated facial recognition has been pressed ahead since the 1960s. Failed attempts and persistently high false-positive quotas have not stopped this storyline. It\u2019s high time for a political discussion.<\/p>\n The SIS \u2013 220 Queries per Second<\/strong> Europe\u2019s largest wanted-persons database has been expanded in recent years. The amount of alerts and queries is rising significantly. Currently, additional functions are being gradually implemented, and the group of users whom access is granted is being broadened. Furthermore, the EU \u201cInteroperability\u201d project connects the SIS with other EU databases. Now, \u201cBrexit\u201d is sowing confusion.<\/p>\n The Club de Berne \u2013 Out of Control<\/strong> Founded in 1969, the Club de Berne is today considered an official network of the domestic intelligence services of the EU countries plus Norway and Switzerland. New documents show that US agencies are involved, too. In the meantime, this guild of secret intelligence agencies disposes of its own operative platform that comes with a database of personal data. Although the latter may have been granted a legal foundation in national laws, there is still no democratic control.<\/p>\n The IT Security Act 2.0<\/strong> Once more, the German parliament is to address the protection of IT systems and the data they store. The 2015 reform of the IT Security Act is now to be followed by another. A draft by the Federal Ministry of the Interior from 27 March 2019 is currently undergoing interdepartmental coordination. The plan is characterized by extensive criminalization of activities in early or preparatory stages, as well as a worryingly broad wording of criminal offences.<\/p>\n","protected":false},"excerpt":{"rendered":" Thematic Focus: Police Data Culture Looking Ahead Through the Data Jungle. Datafication and Prevention \u2013<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[125,149],"tags":[],"class_list":["post-17223","post","type-post","status-publish","format-standard","hentry","category-cilip-121","category-summaries"],"_links":{"self":[{"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=\/wp\/v2\/posts\/17223","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=17223"}],"version-history":[{"count":0,"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=\/wp\/v2\/posts\/17223\/revisions"}],"wp:attachment":[{"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=17223"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=17223"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp-dev.daten.cool\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=17223"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
\nby Benjamin Derin, Christian Meyer and Friederike Wegner<\/p>\n
\nby Dirk Burzcyk<\/p>\n
\nby Stephanie Schmidt<\/p>\n
\nInterview with the data protection commissioner of Hamburg<\/p>\n
\nby Nina Galla<\/p>\n
\nby Roland Meyer<\/p>\n
\nby Matthias Monroy<\/p>\nNon-Thematic Contributions<\/h4>\n
\nby Jan Jir\u00e1t and Lorenz Naegeli<\/p>\n
\nby Louisa Zech<\/p>\n