For the past decade, the anti-trafficking movement has been hindered by a lack of robust, accurate, and standardized data, which has in turn made it challenging to design successful policies to combat this severe exploitation. The combination of the Human Trafficking Data Lab’s skillset and data access enables quantitative empirical research at a scale and caliber not previously possible in the anti-trafficking field. Furthermore, the Lab’s path from science to impact includes policy engagement at the local, national, and international levels – in other words, we are both working to better understand what makes people vulnerable to trafficking, how trafficking markets work, and how well current anti-trafficking efforts are – or are not – working, while also developing initiatives with local partners to improve outcomes.

First-of-its-Kind Anti-Trafficking Data Hub

The Human Trafficking Data Lab’s research-enabled Data Hub offers an unprecedented glimpse into the complex world of  trafficking, uniting thousands of real trafficking cases with intricate details that shed light on the challenges survivors face and the tactics used by perpetrators.

Focusing on demographics, socio-economic context, social program coverage, geospatial patterns, environmental interconnections, policy changes over time, and enforcement practices, we can help build informed decisions and drive effective change.

Our ambitious data architecture transcends conventional analysis regarding business practices associated with trafficking. Delving into registries and uncovering the intricate web of legal entities, ownership networks, and asset management schemes, we aim to shine a light on the hidden networks and supply chains that violate human rights, granting unparalleled evidence-based insights to policymakers and fostering better governance and social sustainability in the private sector.

The seamless integration and expansion of terabytes within the Lab’s Data Hub drive continuous advancements in research, empowering us to help combat one of the most complex and elusive crimes of our time.

The application of this rigorously crafted architecture and data strategy to Brazil, with a special focus on trafficking in the Brazilian Amazon, has helped us move towards a global model for anti-trafficking data-strategies, research-validated knowledge, and evidence-based interventions. In doing so, we infuse new life into methodological approaches while keeping the human context at the forefront.

Better Targeted Anti-Trafficking Inspection Efforts

Leveraging human-centered artificial intelligence, the Human Trafficking Data Lab pioneered a cutting-edge decision-support solution designed specifically for anti-trafficking agencies. This dynamic tool harnesses five powerful AI components:

  • Network model to map and understand the intricate connections between various entities involved in trafficking: it unveils hidden patterns, relationships, and potential hubs of illicit activity;
  • GIS spatial-temporal model aimed at analyzing geographical data over time to track and predict trafficking routes and hotspots. Its main goal is to provide a representation of trafficking trends and their evolution in specific regions;
  • Real-time natural language processing model designed to scan, interpret, and extract valuable information from vast amounts of unstructured data in real-time. As such, it promptly identifies potential threats and any relevant intelligence.
  • Regression models to predict trafficking incidents based on rigorously prepared data. By understanding trends, anti-trafficking agencies can allocate resources more efficiently and take proactive measures.
  • Deep-learning ‘kitchen sink’ model that integrates various data types and employs deep learning techniques to discern complex patterns and relationships. Its objective is to offer a comprehensive analysis and derive insights that might be missed by other models.
Through the comprehensive analysis of patterns across sectors, geographies, supply chains, and intricate network relationships, our AI pinpoints the most suspicious activities, demanding immediate and prioritized action. Amid trafficking operations that expertly hide victims and evade detection, traditional methods often left law enforcement chasing shadows. Armed with our research-driven technology, authorities can discern the cases most likely linked to genuine trafficking activities, gauging their urgency. This clears the way for prompt, data-driven interventions and optimal resource allocation. Our commitment to efficacy is further solidified by rigorous randomized control trials among agencies’s units, cementing our technology’s impactful effectiveness.

Using AI to Detect Human Trafficking from Space

The remote detection technology born in the Human Trafficking Data Lab, powered by AI neural networks, utilizes a computer vision model trained on satellite imagery and geospatial data. The tool accurately pinpoints zones in the Brazilian Amazon where illegal deforestation and forced labor likely converge in producing charcoal used in the steel supply chain. Piloted with research rigor in collaboration with Brazilian authorities, this technology allows for a more proactive anti-trafficking intervention, moving beyond merely reactive, tip-driven initiatives.

Every year, approximately 5,000 square miles of native Brazilian rainforest are cleared and converted into agricultural and cattle ranching land, a process intertwined with violence and exploitative labor. Identifying these critical intersections amplifies our efforts to counter both labor exploitation and the environmental repercussions of tree loss and charcoal burning, thus marking a profound advancement towards social and environmental sustainability in the region.

Improved Outcomes for Survivors

In partnership with frontline anti-trafficking actors and civil society organizations in Brazil, the Human Trafficking Data Lab is piloting a trauma-informed, survivor-centered peer counseling program. The objective is to elevate the mental health and economic outcomes for trafficking survivors. This initiative introduces a rigorous data strategy and sets the gold standard for the survivor support systems currently utilized by various local governments and civil society organizations.

The initiative features questionnaires designed to assess and monitor survivors’ economic conditions, health needs, and mental health indicators. These evaluations, securely administered following trafficking incidents and during subsequent follow-up check-ins, are supervised by trained peer counselors.

Such initiatives are essential: after exiting exploitative situations, survivors often grapple with obtaining holistic, quality care, including essential services. Proper support acts not just as a protective layer but is also pivotal for prevention, particularly given the high risk of retrafficking.

Assessing Accountability Mechanisms

Leveraging time-series stock price data, court records, and corporate ownership network data, the Human Trafficking Data Lab is examining the repercussions of Brazil’s Dirty List on both publicly and privately held companies. This in-depth research is anticipated to yield the first comprehensive mapping of trafficking networks prevalent in industry-specific supply chains.

Traditionally, in the battle against human trafficking, naming-and-shaming “dirty lists” can be employed. These lists publicly document companies found using forced labor and, in Brazil’s context, enforce credit and other country-level sanctions, not to mention foreign trade repercussions. Our focus extends beyond just understanding the impact: we are also delving into corporate strategies that aim to circumvent the financial ramifications imposed by such lists.

Impact Evaluation of Social Programs

The Human Trafficking Data Lab is actively engaging in impact evaluations of Brazil’s primary anti-poverty programs to empirically validate the efficacy of poverty alleviation as a tool against human trafficking. Central to our assessment is Brazil’s flagship conditional cash transfer program.

Utilizing longitudinal data on households, our approach encompasses a regression discontinuity design analysis, harnessing specifics of the program’s eligibility criteria, ensuring a credible methodology for addressing any manipulation of eligibility. This initiative seeks to provide empirical evidence for a widespread belief within anti-trafficking circles: that poverty alleviation stands as a pivotal deterrent to trafficking.

Improved Trafficking Prevalence Estimation

As a core member of the US State Department’s Prevalence Research and Innovation Forum, the Human Trafficking Data Lab is pioneering a methodical approach to primary data collection centered on human trafficking prevalence estimation.

Our initiative encompasses a comprehensive representative survey targeting labor trafficking prevalence within Brazil’s agricultural sector. As we proceed, we are fostering collaborative dialogues with key research partners, including statistical agencies and esteemed research institutions, to bolster the accuracy and depth of our study.

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Lizzy Constantz, MPH

Lizzy Constantz serves as the Program Manager for the Stanford Human Trafficking Data Lab. She is a graduate of the Johns Hopkins Bloomberg School of Public Health, where she studied human trafficking and human rights, culminating in a Masters thesis analyzing the correlates of early child marriage in Ethiopia. Prior to her work in public health, Lizzy developed an expertise in translation and clinical research, as well as programs and operations management. As Program Manager, she is eager to use her background and education to advance the lab’s initiatives.