Strengthening Evidence to Tackle Labour Exploitation and Human Trafficking
There are more people trapped in slavery-like working and living conditions today than ever before. Yet, of an estimated 24.9 million victims, less than 1% are being identified and subsequently helped. To address this global human rights violation, the United Nations’ 2030 Agenda for Sustainable Development includes Target 8.7, which commits the international community to “take immediate and effective measures to eradicate forced labour, end modern day slavery and human trafficking”. How can we strengthen the effectiveness of our efforts to identify victims?
To answer this question, United Nations University Institute in Macao and The Mekong Club started our work in Thailand with a broad cross-section of stakeholders—whom we refer to as frontline responders (FLRs), FLRs, with the aim of understanding why existing victim-identification practices were failing to find victims and identify exploitation. From initial stakeholder consultations, our landscape analysis revealed four main issues affecting the screening of potential victims of human trafficking and labour exploitation.
First, FLRs faced communication issues as they were unable to speak the same language as workers. Second, initial screening often occurs in the field and sometimes in front of potential exploiters, leading to privacy concerns. Third, FLRs and workers often mentioned a lack of trust in each other’s intentions. Finally, FLRs raised concerns of a lack of training and understanding of the common indicators of labour exploitation and forced labour. Based on these findings, we worked with key stakeholders to develop a tool to support the initial screening interview in victim identification—enabling FLRs to overcome communication, training and privacy issues—and to build trust between FLRs and potential victims.
Data gathering for effective victim-identification
Apprise is a smart-phone based, expert knowledge-based system, developed to enable frontline responders to proactively and consistently screen workers for signs of human trafficking and forced labour. In partnership with industry experts, anti-trafficking practitioners and survivors, we developed a list of questions to assess working conditions and calculate a vulnerability rating based on workers’ responses. After completing this screening using Apprise, the worker is informed of the vulnerability of their situation, and the frontline responder receives specific information on which indicators of exploitation have been flagged and recommendations on how to proceed. All screening responses are uploaded to the FLRs’ account for post-hoc analysis. We have been testing Apprise in the fishing, seafood processing, manufacturing and sex work sectors since March 2018, with more than 2,000 workers using the tool.
A Cambodian fisher described the importance of systems like Apprise in protecting workers’ privacy, saying: “I don’t like telling my story repeatedly to others. I know that it is important to talk to them but it is bitter to talk about things I wish to forget. Sometimes a face-to-face conversation is much harder than I expected. My privacy is protected when the interview is done without others knowing my answer.”
Similarly, a Burmese FLR in the fishing and seafood processing sector spoke about the use of Apprise for case referral: “I use the app in my phone to interview them if I think they might need help. If it shows that they are highly vulnerable, I immediately refer them to the case management team.”
In the manufacturing industry, we tested the app with global corporations where we found that Apprise could improve the effectiveness of worker screening during social compliance audits to identify exploitation in supply chains. In one instance, the app allowed an auditor in Thailand to screen female workers and identify issues affecting them, such as harassment and forced pregnancy testing. Using that information, the auditor made recommendations to the factory management to change health check forms to ensure workers were not subjected to pregnancy tests, and to run a refresher course on their zero-tolerance policy on harassment and abuse. This is a clear example of how the tool enabled the FLR to make more informed decisions on the spot.
In working closely with FLRs in the field for over a year, we noted that exploiters change their patterns of abuse over time to avoid detection. We began to question if there was a role for digital technology to detect sector-specific patterns of exploitation and how they change over time and region.
Uncovering patterns of exploitation
To begin exploring this pattern detection, we initially extended Apprise to perform simple statistical analytics of anonymous screening responses. A registered user is currently able to analyse the results of their organization’s interviews, filtering them by criteria, to determine the most often reported forms of exploitation in their result set. We plan to extend this system to include a machine learning component to support deeper analysis. Machine learning systems work by generalizing patterns that they detect in data. If used in data collected with Apprise, machine learning could potentially identify: how patterns of exploitation change; where new hot spots emerge; and vulnerable populations and groups.
Strengthening evidence to inform policy response
The United Nations Office on Drugs and Crime notes that current use of data and statistics in anti-trafficking can help FLRs to identify problems, but what is required is “a systematic way to collect and share strategic information… to evaluate the [FLRs’] capacity to respond to the problem”.
Apprise promotes proactive and consistent screening of workers in vulnerable situations, overcoming issues with communication, privacy, trust and training in the initial screening phase of victim identification. By bolstering the systematic collection and sharing of reported cases of exploitation, Apprise supports policymakers to analyse and convert reports into evidence that is accessible and useful to inform policy response.
Dr Hannah Thinyane is a Principal Research Fellow at United Nations University Institute in Macau.
Francisca Sassetti was a Research Assistant at the United Nations University Institute in Macau.
This article has been prepared by Dr Hannah Thinyane as a contributor to Delta 8.7. As provided for in the Terms and Conditions of Use of Delta 8.7, the opinions expressed in this article are those of the author and do not necessarily reflect those of UNU or its partners.