Introduction

Modern slavery, described as one of the cruellest forms of exploitations, encompasses forced labour and marriage, sexual exploitation, debt bondage and sale and exploitation of children. In 2016, it was estimated that 40.3 million people were modern day slaves at any given point in time globally. Several countries with the highest estimates are those riddled with conflict and suffering from a lack of security and breakdown in the rule of law, resulting in displacement and high risk of exploitation. This causes both national and international risk, particularly as both people and/or the products that they are forced to make in deplorable environments are bought and sold and moved across land, air and waters.

New technologies, rising smartphone ownership and cheap, fast internet use has taken slavery into modern digital age where there are potentially more risks for exploitation, more victims, more perpetrators, and customers, and where traditional law enforcement and intelligence techniques and capabilities are not as strong as they are offline. Whilst technology is facilitating modern day enslavers to entrap more victims through enticement, expand their illicit organisations and hide behind screens, new technologies and data streams from the digital footprint left by users and machine learning methods is also yielding “tech” solutions to enable law enforcement and intelligence agencies to engage with researchers to disrupt the recruitment and continued enslavement, with potential to shift the risk from victims to increased risk to enslavers and traffickers, and thereby reducing harm to potential victims and the wider community.

This project focuses on using and developing data science and machine learning methods to understand and identify risks and vulnerabilities to enable and inform policy and operational insights and approaches in the prevention and pursuit of modern slavery, human trafficking, and related serious exploitative and organised behaviours. The challenge is to integrate and often work with patchy, small and disparate data sources.

Following an initial workshop and continued stakeholder engagement, three main streams of work were identified:

  • Using machine learning methods to identify national drivers of modern slavery
  • Mapping and estimating vulnerability to exploitation
  • Participatory AI in tackling sexual exploitation online

The project will aim to develop and deliver innovative tools, allowing policy makers and front line researchers to better assist in the prevention, pursuit, prosecution of perpetrators and support of victims.

Organisers

Researchers and collaborators

James Goulding

Deputy Director of N-Lab and Lecturer of Analytics, University of Nottingham