Big data has been celebrated for its potential on countless occasions – for its capacity to eliminate food waste, reduce fraud and cyberattacks, disrupt urban planning, and to revolutionise the healthcare industry through machine learning. But today it is being lauded for something even greater – to eliminate the global problem of human trafficking.
Until now, the traditional tactics for identifiying and preventing instances of human trafficking have fallen short of expectations. They have, generally speaking, failed. Failed because those leading the exploitation game are still winning at it, through trapping mostly women and children into domestic servitude by luring them with the prospect of money, education and a ”’better life’.
Sex trafficking and commercial sexual exploitation are part of a growing global phenomenon of modern slavery impacting communities from all corners of the globe, making trafficking one of the most serious human rights abuses of the 21st century. Time and time again, the problem of human trafficking has been called a pandemic – a horrific human rights abuse that is not only a crime in itself, but that usually leads to further abuse and crimnes, such as enslavement, sexual violence and physical violence.
UNICEF estimates that globally there are around 21 million trafficked people today, including 5.5 million children. According to the Global Slavery Index, India has an estimated 18.3 million slaves, and every year around 200,000 Indian children are tricked, kidnapped, or coerced into slavery. In 2018, Thailand experienced a record number of human trafficking cases, with roughly 60% of those rescued being women, who were mostly trafficked for seafood industry or sex trade. The United States – a country many regard as highly developed and therefore immune to such horrific human rights abuses – was ranked one of the world’s worst places for human trafficking in 2018, alongside the Philippines and Mexico.
But thanks to big data, governments and organisations worldwide could make a real impact in putting an end to all that suffering.
The team at Edelman Predictive Intelligence Centre (EPIC) and STOP THE TRAFFIK (STT) are using disruptive technology to capture intelligence on who is trafficking, how, when, and where, in a bid to put an end to international labour trafficking. By collaborating with various NGOs to analyse collated data and combine it with open source data, the initiative is attempting to target the recruitment stage of trafficking operations, through better understanding where and how exactly it happens. Using big data, the operation identifies trafficking ”’hotspots’ and simultaneously launches digital awareness campaigns in those areas, to help potential victims and the general public recognise the warning signs and – hopefully – clock on to what is happening before it happens.
So far, collected data has shown that handbags and smartphones are often ”’gifted’ to those being recruited in the hopes of winning trust. By using this information and setting up geo-targeted campaigns, as well as setting up a search tool to identify when those keywords are used in digital communications, it is easier for local and international law enforcement to identify who is engaging in these kind of interactions.
Global Emancipation Network (GEN), and the National Center For Missing and Exploited Children (NCMEC) are other examples of non-profit organisations using big data to identify human traffickers and their victims. Using Minerva, GEN and NCMEC have jointly identified 989 individual victims and perpetrators and are currently tracking 22,000 more. How? Through locating and analysing phone numbers linked to online advertisements, and searching through hard-to-search data sets for identifying information, Minerva can find instantaneously what people have until now taken months, if not years, to find – and by then, it is usually too late.
Of course, as with anything that involves information gathering and potential privacy breaches, there will be much opposition – particularly from those who are invested in shielding their private activities and digital interactions. For businesses in particular, reputation management will be a major concern, and for individuals, the right to personal privacy. Together with the power that comes with access to such data comes great responsibility, and it is hoped by all that access to this type of technology continues to be leveraged only by ‘the good guys’
– ie. those fighting for change for those too vulnerable to be able to do so themselves.
If big data analysis capabilities are limited only to those non-profit organisations and businesses seeking to make a positive impact on the world through preventing further instances of human trafficking – and if they need to pass a lengthy and complicated ”’application’ or interview process to be able to do so – I think we can safely say the technology will remain in the hands of the right people. But if big data analysis technologies fall into the wrong hands, in other words, that of the traffickers themselves or of businesses and government agencies seeking to uncover other types of personal information, then we would need to re-address the situation.