Using US Artificial Intelligence to Fight Human Trafficking in Europe

• Date: 06 March 2023
• Year: 2023
• Author: Salomé Lannier
• Pages: 69
• DOI: https://doi.org/10.30709/eucrim-2023-002

I. Introduction

In public international law, sovereignty derives from the independence and autonomy of States. The parallel aspect of enjoying the monopoly of legitimate authority over a territory is the exclusion of other States’ authority.¹ At the core of the autonomy of States’ sovereignty lies their criminal sovereignty: defining offences, sanctions, powers of investigation, policies, priorities, etc.² Yet, sovereignty was mainly conceptualised in the 16th century,³ and such idealization of States’ autonomy strikes us a utopia in our globalised⁴ and digitalised⁵ world. Consequently, the concept of digital sovereignty was developed to adapt to new realities. Originally meant as informational sovereignty (control over information⁶), today digital sovereignty covers different concepts, such as technological sovereignty and data sovereignty,⁷ due to the lack of a uniform use. In this article, the modern-day theory of (digital) sovereignty will allow us to highlight the contradiction between the supposed autonomy of States and the “de facto disparities of power among States, which, in turn, might limit their capacity to act, to regulate and to freely adopt decisions.”⁸ These disparities of power are particularly threatening to independent sovereignty when they impact criminal law, which is seen as being at the heart of the State’s monopoly of legitimate violence.⁹

One of these disparities of power lies in the ability to develop, to use, and to regulate artificial intelligence (AI) systems when applied to repress criminal offences. Since AI relies on humans and institutions for its creation and functioning, “it depends entirely on a […] set of political and social structures.”¹⁰ While no unique definition exists regarding AI,¹¹ computer systems have been assisting States’ decision-making processes since the 1970s.¹²

There are many examples of AI systems in use to support the prevention and prosecution of offences. Human trafficking (in particular for the purpose of sexual exploitation) is taken as an example in this article to draw conclusion on the use of AI systems for law enforcement purposes, as they have received little attention from legal scholars (in this area) until now. Human trafficking is an internationally criminalised offence defined in the 2000 Protocol to Prevent, Suppress and Punish Trafficking in Persons, Especially Women and Children, supplementing the United Nations Convention against Transnational Organized Crime (Art. 3.a). It is defined as follows:

[t]he recruitment, transportation, transfer, harbouring or receipt of persons [element 1: actions], by means of the threat or use of force or other forms of coercion, of abduction, of fraud, of deception, of the abuse of power or of a position of vulnerability or of the giving or receiving of payments or benefits to achieve the consent of a person having control over another person [element 2: coercive means], for the purpose of exploitation [element 3: purpose].

Therefore, trafficking represents a security threat violating the human rights of victims. Protecting victims and prosecuting perpetrators is a manifestation of States’ criminal sovereignty. Nowadays, the fight against human trafficking is also at the crossroads of States’ digital sovereignty. Indeed, technologies, in particular the internet, can exacerbate the trafficking schemes. Consequently, the term e-trafficking was “coined to describe human trafficking facilitated/enabled or regulated through the use of the internet and other communication platforms.”¹³ To recruit victims, traffickers actively impersonate an employer, rely on cyber seduction,¹⁴ or use different types of bait online, usually a false job offer.¹⁵ The internet is used to book transportation and accommodation for the potential victim.¹⁶ During the exploitation stage, when the victims are trafficked for the specific purpose of sexual exploitation, technology enables their sexual services to be advertised online.¹⁷ Although trafficking encompasses many forms of exploitation (sexual exploitation, labour exploitation, forced begging and criminality, etc.), American AI systems exclusively, as far as we know, focus on the repression of the federal offence of “sex trafficking.” Thus, the intended comprehensive approach of the human trafficking phenomena adopted by this article is limited by the existing technologies. Traffickers might take advantage of technology for the anonymity it provides or to hasten trafficking processes. However, e-trafficking also creates data that might be helpful to investigators and used as evidence. Yet, the sheer volume of data challenges their productive analysis by law enforcement authorities.

The creation of AI systems was intended as a solution, namely to support the fight against human trafficking facilitated by the internet. It can automate the crawling and processing of data, organise information linked to ongoing cases, or improve the detection of patterns and red flags to multiply proactive investigations. This idea was first developed by researchers in the United States in 2012.¹⁸ Later, their elaboration was framed into the Defense Advanced Research Projects Agency.¹⁹ Currently, similar systems are being developed outside of the US (e.g. in Canada²⁰), and US systems are being exported to Europe (e.g. to the United Kingdom and to Ireland²¹). However, the actual or potential use of foreign tools, especially within the European Union, is not neutral with respect to the autonomy of European sovereignties. The following two sections analyse the risks inherent in the use of US AI systems to the criminal national sovereignty and the digital European sovereignty.

II. Risks of Influencing European Criminal Sovereignty

First, the spread of US AI systems developed to support the investigation and prosecution of sex trafficking questions the protection of European national criminal sovereignty. AI systems might be seen as neutral, as they are based on objective data and criteria to combat well-defined criminal phenomena. However, such a perspective reflects mere technological solutionism;²² it “would postulate the existence of a technical solution to any problem.”²³ However, these systems are actually not neutral, as they might be imbued with political positions and policies. As such, when they are used abroad, the politics of their State of origin might be applied in the States of reception, potentially impacting the latter’s autonomous sovereign powers. This risk genuinely exists regarding AI systems designed to prevent and prosecute human trafficking.

Despite benefiting from an international definition, the offence of human trafficking has not been fully harmonised. Firstly, the 2000 Protocol was adapted and broadened by European texts²⁴ (the addition of types of exploitation and suppression of the criterion of a transnational traffic). Secondly, even within Europe, national definitions reveal a wide variety of transpositions of the Directive 2011/36/EU.²⁵ For instance, in Belgium, coercive means are not an element of the offence but an aggravating circumstance.²⁶ In France and in Spain, as in the supranational definitions, these means are part of the elements of the offence, although they are slightly differently defined.²⁷ A comparison between the European definitions and the US code is particularly striking; the latter only recognises trafficking in the context of, on the one hand, peonage, slavery, involuntary servitude, or forced labour, and, on the other hand, sex trafficking.²⁸ Therefore, an AI system to combat human trafficking needs to be adaptable to national definitions, which might not be applicable, as most of them were developed in the United States and for the United States.

The development of such systems is based on the criminal realities and priorities of each country, particularly regarding the types of exploitation. For instance, in Europe, there is a stronger focus on trafficking for labour exploitation.²⁹ Yet, systems of AI financed in the United States exclusively focus on the repression of trafficking for domestic sexual exploitation.³⁰ One of the major means is the analysis of classified advertisements. In particular, these US AI systems emphasise the identification of victims who are minors.³¹ The fact that the existing systems are mainly made in the United States impacts worldwide priorities in the fight against the complex and multifaceted phenomenon of human trafficking. It reinforces the continuous focus on sexual exploitation,³² which has been strongly criticised as a very limited conception of human trafficking.³³

In the latter context, one should consider as well that trafficking for sexual exploitation can, under some national legislations, be conflated with sex work. Certain states’ policies consider commercial sex as exploitative per se, regardless of working conditions and the legitimacy of a sex workers’ agency.³⁴ This is the case in the United States, where sex work is mainly illegal.³⁵ On the contrary, there are various sex work regulations in Europe: legal regulation (the Netherlands, Germany), prohibition (Romania), criminalisation of clients (France, following the Nordic model)³⁶, and decriminalization (Belgium³⁷). To qualify as an act of adult sex trafficking in the United States, the US code only requires a commercial sexual act as the purpose. Yet, it still requires proof of “means of force, threats of force, fraud, [or] coercion”³⁸ (child trafficking does not require this element: to identify an underage trafficked victim, an AI system would only have to detect underage persons advertised for a commercial sexual act). Nevertheless, indicators of potential trafficking in advertisements for sex workers’ services hardly take this element into consideration; they rely only on indirect potential flags of exploitation³⁹ (it is obviously rare to find explicit proof of coercion in the ads). They have been identified on the...