The Invisible Arms Race: Digital Trends in Illicit Goods Trafficking and AI-Enabled Responses

Abstract

Recent trends in the modus operandi of technologically-aware criminal groups engaged in illicit goods trafficking (e.g., firearms, drugs, cultural artifacts, etc.) have given rise to significant security challenges. The use of cryptocurrency-based payments, 3D printing, social media and/or the Dark Web by organized crime leads to transactions beyond the reach of authorities, thus opening up new business opportunities to criminal actors at the expense of the greater societal good and the rule of law. As a result, a lot of scientific effort has been expended on handling these challenges, with Artificial Intelligence (AI) at the forefront of this quest, mostly machine learning and data mining methods that can automate large-scale information analysis. Deep Neural Networks (DNNs) and graph analytics have been employed to automatically monitor and analyze the digital activities of large criminal networks in a data-driven manner. However, such practices unavoidably give rise to ethical and legal issues, which need to be properly considered and addressed. This paper is the first to explore these aspects jointly, without focusing on a particular angle or type of illicit goods trafficking. It emphasizes how advances in AI both allow the authorities to unravel technologically-aware trafficking networks and provide countermeasures against any potential violations of citizens’ rights in the name of security.