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The AI-shift: how Europol is leveraging artificial intelligence to combat serious organised crime and terrorism

Dános, Valér (2024) The AI-shift: how Europol is leveraging artificial intelligence to combat serious organised crime and terrorism. BELÜGYI SZEMLE / ACADEMIC JOURNAL OF INTERNAL AFFAIRS: A BELÜGYMINISZTÉRIUM SZAKMAI TUDOMÁNYOS FOLYÓIRATA (2010-), 72 (9). pp. 1699-1705. ISSN 2062-9494

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Abstract

Aim: The aim of this article is to highlight the importance of leveraging Artificial Intelligence (AI) in law enforcement to combat serious organised crime and terrorism, while ensuring responsible and accountable use of AI tools through collaboration and knowledge-sharing among European law enforcement agencies.Methodology: The study uses a descriptive methodology to describe the development and cooperation process through which the Innovation Lab contributes to the innovation development and knowledge sharing of Europol and its member countries.Findings: With the increasing volume and speed of investigative data, AI has emerged as a promising solution to help law enforcement agencies process and analyse large and complex datasets. Europol has been at the forefront of developing and sharing AI tools with its Member States, ensuring their responsible and accountable use. The integration of Artificial Intelligence (AI) in law enforcement investigations has been found to significantly enhance the efficiency and effectiveness of crime fighting, particularly in processing and analysing large and complex datasets.Value: The article highlights the importance of collaboration and knowledge-sharing among law enforcement agencies to keep pace with AI advancements and prevent criminal abuse of these technologies.

Item Type: Article
Uncontrolled Keywords: Artificial Intelligence (AI), law enforcement, Europol, Innovation Lab
Subjects: J Political Science / politológia > JF Political institutions (General) / politikai intézmények, államigazgatás általában
SWORD Depositor: MTMT SWORD
Depositing User: MTMT SWORD
Date Deposited: 18 Sep 2024 13:40
Last Modified: 18 Sep 2024 15:09
URI: https://real.mtak.hu/id/eprint/205211

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