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1. Advancing interdisciplinary science for disrupting wildlife trafficking networks

   Gore, M.L. (January 2023, Proceedings of the National Academy of Sciences of the United States of America)

   Agrawal, A. (Ed.)

   Wildlife trafficking, whether local or transnational in scope, undermines sustainable development efforts, degrades cultural resources, endangers species, erodes the local and global economy, and facilitates the spread of zoonotic diseases. Wildlife trafficking networks (WTNs) occupy a unique gray space in supply chains—straddling licit and illicit networks, supporting legitimate and criminal workforces, and often demonstrating high resilience in their sourcing flexibility and adaptability. Authorities in different sectors desire, but frequently lack knowledge about how to allocate resources to disrupt illicit wildlife supply networks and prevent negative collateral impacts. Novel conceptualizations and a deeper scientific understanding of WTN structures are needed to help unravel the dynamics of interaction between disruption and resilience while accommodating socioenvironmental context. We use the case of ploughshare tortoise trafficking to help illustrate the potential of key advancements in interdisciplinary thinking. Insights herein suggest a significant need and opportunity for scientists to generate new science-based recommendations for WTN-related data collection and analysis for supply chain visibility, shifts in illicit supply chain dominance, network resilience, or limits of the supplier base. 

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2. Towards ethical and resilient agricultural supply chains: interdicting labor trafficking and mitigating disruptions

   https://doi.org/10.17760/D20705224  

   Jafari, Arezoo (December 2024, Northeastern University Digital Repository Service)

   The food and agriculture industries are critical to the U.S. economy, ensuring the daily food supply while facing significant challenges. These issues include ethical concerns related to labor exploitation and the need to improve resilience against disruptions. Addressing these issues offers an opportunity to create supply chains that are both more ethical and more resilient. This dissertation focuses on two interconnected aspects of agricultural supply chains. The first examines strategies for disrupting exploitative labor practices and ensuring better protection for farm workers. The second explores methods to enhance the resilience of ethical supply chains against various disruptions, including natural disasters and labor shortages. Together, these aspects aim to contribute to the development of agricultural supply chains that are both ethically sound and resilient to disruptions. Although farm workers play an essential role in the success of these industries, they are vulnerable to labor exploitation and trafficking. Labor violations affecting these workers often go undetected due to limited government resources for inspection. Furthermore, many farm workers face barriers to disclosing their poor working conditions due to their immigration status and mistrust of law enforcement, making them even more susceptible to exploitation. To address this issue, we conducted research to provide strategies for government agencies involved in the H-2A visa program and the screening of H-2A employers to prioritize workplaces for inspection. In the first study, we employed multilevel zero-inflated negative binomial regression analysis to extract patterns and identify factors correlated with detecting H-2A labor violations. We provide suggestions for improving inspection strategies based on our research results. This involved identifying high-risk locations and labor-intensive worksites with a greater likelihood of labor violations and emphasizing the importance of allocating sufficient task force funding and resources to prioritize inspections in these areas. Labor trafficking networks in U.S. agricultural supply chains exploit vulnerable workers, including migrants and unauthorized laborers, while evading detection through complex structures, making them difficult to disrupt. In the second study, we developed a comprehensive labor trafficking network model that maps the intricate connections and operations of these networks. Using a bi-level integer programming approach, we optimized intervention strategies to disrupt trafficking operations, balancing resource constraints with the need for maximum impact. By employing K-means clustering, we classified interventions based on their effectiveness, providing clear, data-driven guidance for anti-trafficking agencies to prioritize efforts and allocate resources efficiently. This approach offers a powerful tool for enhancing detection and improving the overall effectiveness of anti-trafficking initiatives in limited resource environments. The importance of food and agricultural supply chains in our daily lives cannot be emphasized enough. While the prior two studies sought to disrupt exploitative work conditions in agricultural supply chains, this dissertation also seeks to help supply chains that are operating ethically do so in an effective manner. Any disruption in these chains can lead to severe consequences, from food shortages to economic instability. Therefore, it is critical to develop effective strategies to mitigate the impact of disruptions in these non-exploitative supply chains. In the third study, we developed a scenario-based two-stage stochastic model to mitigate the impact of multiple disruptions in agricultural supply chains. This approach enables a detailed evaluation of strategies such as multi-sourcing and the use of backup facilities to reduce disruption impacts. The model incorporates flexibility to handle both partial and full facility disruptions, while accounting for disruptions affecting both primary and backup facilities to provide a comprehensive analysis of supply chain vulnerability and recovery. By employing a multi-period time horizon, the model evaluates supply chain performance over time, considering random disruption start times and the possibility of simultaneous disruptions across multiple echelons with varying severity. The analysis highlights the challenges posed by multiple sources of uncertainty in supply chain decision-making and emphasizes the need for further research to develop actionable strategies for improving resilience in agricultural supply chains. 

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3. Detecting Human Trafficking: Automated Classification of Online Customer Reviews of Massage Businesses

   https://doi.org/10.1287/msom.2023.1196  

   Li, Ruoting; Tobey, Margaret; Mayorga, Maria E.; Caltagirone, Sherrie; Özaltın, Osman Y. (May 2023, Manufacturing & Service Operations Management)

   Problem definition: Approximately 11,000 alleged illicit massage businesses (IMBs) exist across the United States hidden in plain sight among legitimate businesses. These illicit businesses frequently exploit workers, many of whom are victims of human trafficking, forced or coerced to provide commercial sex. Academic/practical relevance: Although IMB review boards like Rubmaps.ch can provide first-hand information to identify IMBs, these sites are likely to be closed by law enforcement. Open websites like Yelp.com provide more accessible and detailed information about a larger set of massage businesses. Reviews from these sites can be screened for risk factors of trafficking. Methodology: We develop a natural language processing approach to detect online customer reviews that indicate a massage business is likely engaged in human trafficking. We label data sets of Yelp reviews using knowledge of known IMBs. We develop a lexicon of key words/phrases related to human trafficking and commercial sex acts. We then build two classification models based on this lexicon. We also train two classification models using embeddings from the bidirectional encoder representations from transformers (BERT) model and the Doc2Vec model. Results: We evaluate the performance of these classification models and various ensemble models. The lexicon-based models achieve high precision, whereas the embedding-based models have relatively high recall. The ensemble models provide a compromise and achieve the best performance on the out-of-sample test. Our results verify the usefulness of ensemble methods for building robust models to detect risk factors of human trafficking in reviews on open websites like Yelp. Managerial implications: The proposed models can save countless hours in IMB investigations by automatically sorting through large quantities of data to flag potential illicit activity, eliminating the need for manual screening of these reviews by law enforcement and other stakeholders. Funding: This work was supported by the National Science Foundation [Grant 1936331]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1196 . 

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4. Interdiction of wildlife trafficking supply chains: An analytical approach

   https://doi.org/10.1080/24725854.2023.2255643  

   Griffin, Emily C; Ferber, Aaron; Lafferty, Lucas; Keskin, Burcu B; Dilkina, Bistra; Gore, Meredith (March 2024, IISE Transactions)

   Illicit Wildlife Trade (IWT) is a serious global crime that negatively impacts biodiversity, human health, national security, and economic development. Many flora and fauna are trafficked in different product forms. We investigate a network interdiction problem for wildlife trafficking and introduce a new model to tackle key challenges associated with IWT. Our model captures the interdiction problem faced by law enforcement impeding IWT on flight networks, though it can be extended to other types of transportation networks. We incorporate vital issues unique to IWT, including the need for training and difficulty recognizing illicit wildlife products, the impact of charismatic species and geopolitical differences, and the varying amounts of information and objectives traffickers may use when choosing transit routes. Additionally, we incorporate different detection probabilities at nodes and along arcs depending on law enforcement’s interdiction and training actions. We present solutions for several key IWT supply chains using realistic data from conservation research, seizure databases, and international reports. We compare our model to two benchmark models and highlight key features of the interdiction strategy. We discuss the implications of our models for combating IWT in practice and highlight critical areas of concern for stakeholders. 

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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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