Dear Dr. Smith,
Dear Ashley,
As in so many weeks, you have demonstrated a very keen sense of intuition and taste in selecting a topic, which comes at the right time, indeed, in dire need, and which calls us at OR-MS to remedy. Indeed, human trafficking is one of the most abhorrent phenomena of our time, and it confronts us in so many forms, often chameleon-like, or manages to hide from us for long periods of time.
Regarding the subject and article, as well as the topic "Unmasking Human Trafficking: New AI Research Reveals Hidden Recruitment Networks," may I perhaps mention another, new approach that combines data science or machine learning on the one hand, and game theory on the other.
In fact, this approach uses the methods of Multivariate Adaptive Regression Spines (MARS) and its new variants CMARS, RMARS, RCMARS, CGPLM, RCPLM, or RCGPLM (developed by my colleagues and me) to model the human trafficking network, and then uses game theory, with the (ideally) "fairness measure" of the Shapley value, to determine "optimal" coalitions. What is ideally "bright" and attractive in scientific research becomes something "dark" and repellent to us in the case of human trafficking.
However, the primary purpose of such an approach and method is to anticipate criminal behavior, particularly the formation of human trafficking networks, for the "dark counterpart" of a "bright game theory," in order to help disrupt them early on. In this regard, game theory leads to an improvement over the results of mere data science or machine learning.
In fact, I can cite a few references in this regard:
M. Graczyk-Kucharska, R. Olszewski, M. Golinski, M. Spychalla, M. Szafranski, G.W. Weber and M. Miadowicz, Human resources optimization with MARS and ANN: Innovation geolocation model for generation Z, Journal of Industrial and Management Optimization (JIMO) 18(6) 2022, 4093-4110; doi: 10.3934/jimo.2021149.
I. Özcan, J. Sledzinski, S.Z. Alparslan, M. Butlewski and G.-W. Weber, Mathematical Encouragement of Companies to Cooperate by Using Cooperative Games with Fuzzy Approach, Journal of Industrial and Management Optimization. Vol. 19, No. 10, October 2023, pp. 7180-7195; doi:10.3934/jimo.2022258.
İ. Özcan, J. Sledzinski, S.Z. Alparslan Gök, A. Meca, G.-W. Weber, M. Butlewski and E. Kocadag, A Game Theory Perspective on Strategic Profit Distribution in Complex IT Projects, Journal of Industrial and Management Optimization (JIMO) Vol. 21, No. 2, February 2025, pp. 1503-1517; doi: 10.3934/jimo.2024135.
G.-W. Weber, İ. Özcan, J.D. Śledziński, M. Graczyk-Kucharska and M. Szafranski, The phenomena of "stochastic uncertainties", Generation Z Reshapes HR Strategies with Spatial Data and a Game Theory Approach, VII Edition of the School of Grey Systems, Poznan University of Technology, May, 22-23, 2025.
But let us be very careful in dealing with evil, even if it occurs "only" in a scientific context.
Cordial thanks to you and the entire INFORMS team which maintains and stimulates this friendly and helpful exchange platform.
With kind regards,
best wishes,
Willi (Gerhard-Wilhelm Weber)
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Gerhard-Wilhelm Weber
Professor
Poznan University of Technology
Poznan
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Original Message:
Sent: 05-23-2025 08:38
From: Ashley Smith
Subject: Unmasking Human Trafficking: New AI Research Reveals Hidden Recruitment Networks
BALTIMORE, MD, May 24, 2025 – Most anti-human trafficking efforts focus on breaking up sex sales; however, new research in the INFORMS journal Manufacturing & Service Operations Management is turning its attention to where trafficking truly begins – recruitment. Using machine learning to analyze millions of online ads, researchers at the University of Pennsylvania have uncovered patterns that link deceptive job offers to sex trafficking networks. By mapping the connections between recruitment and sales locations, the study reveals a hidden supply chain – one that can now be exposed and interrupted earlier in the trafficking process.
Click here to read the full press release.
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Ashley Smith
Public Relations Specialist
INFORMS
Baltimore MD
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