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1. Learning Each Other’s Language and Building Trust: Community-Engaged Transdisciplinary Team Building for Research on Human Trafficking Operations and Disruption

   https://doi.org/10.1177/16094069221101966  

   Martin, Lauren; Gupta, Mahima; Maass, Kayse L.; Melander, Christina; Singerhouse, Emily; Barrick, Kelle; Samad, Tariq; Sharkey, Thomas C.; Ayler, Tonique; Forliti, Teresa; et al (January 2022, International Journal of Qualitative Methods)

   Background: Human trafficking for sexual exploitation (referred to as sex trafficking) is a complex global challenge that causes harm and violates human rights. Most research has focused on victim-level harms and experiences, with limited understanding of the networks and business functions of trafficking operations. Empirical evidence is lacking on how to disrupt trafficking operations because it is difficult to study; it is hidden and dangerous, spans academic disciplinary boundaries, and necessitates ways of knowing that include lived experience. Collaborative approaches are needed, but there is limited research on methods to best build transdisciplinary teams. Aim: The aim of this study was to understand how to form a community-engaged transdisciplinary research team that combines qualitative and operations research with a survivor-centered advisory group. Methods: We conducted a qualitative meta-study of our team that is seeking to mathematically model sex trafficking operations. Data were collected from the minutes of 16 team meetings and a survey of 13 team members. Results: Analysis of meeting minutes surfaced four themes related to content and style of communication, one related to value statements, and one capturing intentional team building efforts. Survey results highlighted respect, trust, integrity, openness and asking and answering questions as key aspects of team building. Results show that an action research approach to team building, focused on trust and communication, fostered effective collaboration among social scientists, operations researchers, and survivors of trafficking. Conclusion: Team building, shared language, and trust are essential, yet often neglected, elements of team science. This meta-study provides important methodological insights on community engaged transdisciplinary team formation to tackle vexing social challenges. 

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2. Coupling agent-based simulation and spatial optimization models to understand spatially complex and co-evolutionary behavior of cocaine trafficking networks and counterdrug interdiction

   https://doi.org/10.1080/24725854.2022.2123998  

   Magliocca, Nicholas R.; Price, Ashleigh N.; Mitchell, Penelope C.; Curtin, Kevin M.; Hudnall, Matthew; McSweeney, Kendra (September 2022, IISE Transactions)

   Despite more than 40 years of counterdrug interdiction efforts in the Western Hemisphere, cocaine trafficking, or ‘narco-trafficking’, networks continue to evolve and increase their global reach. Counterdrug interdiction continues to fall short of performance targets due to the adaptability of narco-trafficking networks and spatially complex constraints on interdiction operations (e.g., resources, jurisdictional). Due to these dynamics, current modeling approaches offer limited strategic insights into time-varying, spatially optimal allocation of counterdrug interdiction assets. This study presents coupled agent-based and spatial optimization models to investigate the co-evolution of counterdrug interdiction deployment and narco-trafficking networks’ adaptive responses. Increased spatially optimized interdiction assets were found to increase seizure volumes. However, the value per seized shipment concurrently decreased and the number of active nodes increased or was unchanged. Narco-trafficking networks adaptively responded to increased interdiction pressure by spatially diversifying routes and dispersing shipment volumes. Thus, increased interdiction pressure had the unintended effect of expanding the spatial footprint of narcotrafficking networks. This coupled modeling approach enabled the study of narco-trafficking network evolution while being subjected to varying interdiction pressure as a spatially complex adaptive system. Capturing such co-evolution dynamics is essential for simulating traffickers’ realistic adaptive responses to a wide range of interdiction scenarios. 

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3. Disrupting Labor Trafficking in the Agricultural Sector: Looking at Opportunities beyond Law Enforcement Interventions

   https://doi.org/10.1080/15564886.2022.2133036  

   Childress, Chase; Farrell, Amy; Bhimani, Shawn; Maass, Kayse Lee (April 2023, Victims & Offenders)

   Law enforcement interventions continue to be the primary mechanism used to identify offenders and illicit businesses involved in human trafficking, yet trafficking continues to be a thriving international operation. We explore alternative mechanisms to disrupt illicit operations and reduce victimization through labor trafficking supply chains using supply chain disruption theory. Using a case study approach to examine one federally prosecuted labor trafficking case in the agricultural sector, we (1) extend criminological concepts of disruption by identifying sources and methods of disruption and (2) inform criminal justice system responses by presenting novel methods of assessing effectiveness of anti-human trafficking policies and programs. 

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4. A Cost-Effective LLM-based Approach to Identify Wildlife Trafficking in Online Marketplaces

   https://doi.org/10.1145/3725256  

   Barbosa, Juliana Silva; Gondhali, Ulhas; Petrossian, Gohar; Sharma, Kinshuk; Chakraborty, Sunandan; Jacquet, Jennifer; Freire, Juliana (June 2025, Proceedings of the ACM on Management of Data)

   Wildlife trafficking remains a critical global issue, significantly impacting biodiversity, ecological stability, and public health. Despite efforts to combat this illicit trade, the rise of e-commerce platforms has made it easier to sell wildlife products, putting new pressure on wild populations of endangered and threatened species. The use of these platforms also opens a new opportunity: as criminals sell wildlife products online, they leave digital traces of their activity that can provide insights into trafficking activities as well as how they can be disrupted. The challenge lies in finding these traces. Online marketplaces publish ads for a plethora of products, and identifying ads for wildlife-related products is like finding a needle in a haystack. Learning classifiers can automate ad identification, but creating them requires costly, time-consuming data labeling that hinders support for diverse ads and research questions. This paper addresses a critical challenge in the data science pipeline for wildlife trafficking analytics: generating quality labeled data for classifiers that select relevant data. While large language models (LLMs) can directly label advertisements, doing so at scale is prohibitively expensive. We propose a cost-effective strategy that leverages LLMs to generate pseudo labels for a small sample of the data and uses these labels to create specialized classification models. Our novel method automatically gathers diverse and representative samples to be labeled while minimizing the labeling costs. Our experimental evaluation shows that our classifiers achieve up to 95% F1 score, outperforming LLMs at a lower cost. We present real use cases that demonstrate the effectiveness of our approach in enabling analyses of different aspects of wildlife trafficking. 

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5. Non-traditional cyber adversaries: Combating human trafficking through data science

   Borrelli, Danielle; Caltagirone, Sherrie (January 2020, Cyber security)

   Human trafficking is a complex and challenging global crime exacerbated by the use of technology. Traffickers utilize technology for scalability, anonymity, and profitability as the Internet, social media platforms and encrypted messaging make the recruitment, exploitation and profit of an individual a low-risk, high-reward enterprise. Counter-trafficking efforts are often siloed approaches, resulting in decentralized information and analysis on the size and scope of trafficking in persons. Resources and tools such as the human trafficking kill chain methodology and Artemis, a machine learning (ML) human trafficking risk classifier, show promising disruption tactics which may also be applied to other asymmetrical threats. Recommendations for centralized data collections methods, inter-agency collaboration, and cybersecurity adjacent legislation are also made. 

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