Digging deep: The picture beneath the data on human trafficking

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Dr Julia Muraszkiewicz | Head of Programme Against Exploitation and Violence

Date: 11 February 2021

Initiatives to bring and use data in local government are emerging all around us. Calls for tenders, discussions in webinars and all-night hackathons are all deriving ways to use data to support better decision making around societal problems. Such problems that councils, townhalls and municipalities must deal with because they deeply affect their areas.

How to solve homelessness? Let’s look at the data. How to address domestic violence? Well, what does the data tell us? Child poverty on the rise? Can we use data to understand why?

And rightly so, data combined with state-of-the-art machine learning and artificial intelligence capabilities can tell us a lot. They can, through the establishment of facts, patterns and detection of dependencies between different phenomena, be used to obtain an initial diagnosis of a problem; including a problem such as human trafficking. Local councils can gain significant impact simply through better data analysis, often of data they have held for years. Councils can combine data sets from across local government and the wider local public sector to population-level analysis, and thus provide frontline staff with a much more widespread picture of people they are supporting.

Nevertheless, data is not the full story. A complete diagnosis and subsequent strategy derivation of a problem like human trafficking requires more than just data and analytics. Data is just the tip of the iceberg. Beneath lie a plethora of questions such as “how does it manifest”, “why is that so” “what bigger and smaller issues is it connected to”.

To truly understand human trafficking, it is not enough to collect data on the nationality of victims, their gender, the method through which they were lured into exploitation. I.e., it is not enough to describe the picture, we have to understand how and why that picture came to be.

A complete analysis, therefore, should begin with a mapping exercise of issues connected to human trafficking; if victims are recruited from the homeless community, we need to look at the problem of homelessness. It does not end there. Looking at homelessness means looking at welfare, housing, mental health, situations of those coming out from prisons, and many others. If we are seeing in our city an increase in victims from a particular country, what else is happening in the city that may help explain this? Is there a sudden property investment by backers from that same country? We need to ponder facets of inequality, integration, and deliberate on policies that marginalize people or erode safety nets.

Thus, to effectively grow the understanding of a problem like human trafficking – and to develop theoretical sophistication in this space – it is necessary to rely on a triangulation of data and contexts. That is, combining information and concepts from different sources and different domains. Only through this do we have the possibility to gain a more reliable and deeper comprehension. Whilst triangulation most often concerns data sources and formats, in this post, it is expanded to include socio-political-economic and cultural contexts and theories. Interweave disciplinary discourses that encircle and are outside the box of discussing human trafficking.

Data and subsequent AI/ML models are too raw to reveal the true social drivers of human trafficking. Sometimes the most important factors are unobservable, cannot be fully captured by facts and figures.

Once we engage in this triangulation a picture will emerge with connecting nodes. It is a big picture, it is complicated, some may say it is overwhelming. The good news? It addresses a lot of issues that local authorities already have in their remit, scattered across different departments.

That is why in developing strategies to address human trafficking, local authorities should use a recipe that makes the most of collective human intelligence and machine intelligence to solve our complex social challenges. This calls for data and data analytics – and yes this is also a call for more data in an array of structures and formats and thus also a call for data sharing through use of privacy-compliant data sharing agreements – combined with insights from across local authority departments and other stakeholders like civil society. This should be not just the immediately apparent ones like social services or those responsible for crime. Include those from areas such as health, environment, labour, transport, business, development and fire and public safety.

The problem faced by local authorities regarding human trafficking is really many problems, falling within a multi-disciplinary scientific scope.

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