The FBI warned transnational gangs who have helped turn a busy commercial strip in Rep. Alexandria Ocasio-Cortez’s New York City district into a crime-infested hot spot.

Youngkin, joined by Virginia Attorney General Jason Miyares and other state attorneys general, compared human trafficking enforcement to addressing transnational gangs. "We must have multi-state and federal support in order to dismantle the networks, not just arrest an individual, we've got to unpack the networks," Youngkin told a crowd of a few hundred. The Trump administration has been a boon to human trafficking enforcement efforts, Youngkin said, noting he met with top Justice Department officials at the White House after the inauguration to discuss the matter and found them receptive. Virginia law enforcement has since been coordinating with the federal government to take down foreign gang operations, which Youngkin said overlaps with the human trafficking space. Youngkin used the example of gang crime inside correctional centers, which he said was the first "thread" his team pulled.

Sex trafficking refers to the use of force, fraud, or coercion to compel an individual to perform in commercial sex acts against their will. Adult service websites (ASWs) have and continue to be linked to sex trafficking, offering a platform for traffickers to advertise their victims. Thus, organizations involved in the fight against sex trafficking often use ASW data when attempting to identify potential sex trafficking victims. A critical challenge in transforming ASW data into actionable insight is text analysis. Previous research using ASW data has shown that ASW ad text is important for linking ads. However, working with this text is challenging due to its extensive use of emojis, poor grammar, and deliberate obfuscation to evade law enforcement scrutiny. We conduct a comprehensive study of language modeling approaches for this application area, including simple information retrieval methods, pre-trained transformers, and custom transformer models. We demonstrate that characteristics of ASW text data allow efficient custom transformer models to be trained with relatively small GPU resources and used efficiently for inference on consumer hardware. Our custom models outperform fine-tuned variants of well-known encoder-only transformer models, including BERT-base, RoBERTa, and ModernBERT, on accuracy, recall, F1 score, and ROC AUC. The models we develop represent a significant advancement in ASW text analysis, which can be leveraged in a variety of downstream applications and research. Introduction Sex trafficking involves the use of force, fraud, or coercion to compel an individual to perform commercial sex services. To effectively combat this problem, law enforcement organizations (LEOs), non-profit organizations (NPOs), and researchers must transform sex ad data into actionable intelligence. Previous research using ASW data has shown that assessing the similarity of ASW ad text is important for linking ads.

AI for good initiatives often rely on the assumption that technical interventions can resolve complex social problems. In the context of human trafficking (HT), such techno-solutionism risks oversimplifying exploitation, reinforcing power imbalances and causing harm to the very communities AI claims to support. In this paper, we introduce the Radical Questioning (RQ) framework as a five step, pre-project ethical assessment tool to critically evaluate whether AI should be built at all, especially in domains involving marginalized populations and entrenched systemic injustice. RQ does not replace principles based ethics but precedes it, offering an upstream, deliberative space to confront assumptions, map power, and consider harms before design. Using a case study in AI for HT, we demonstrate how RQ reveals overlooked sociocultural complexities and guides us away from surveillance based interventions toward survivor empowerment tools. While developed in the context of HT, RQ's five step structure can generalize to other domains, though the specific questions must be contextual. This paper situates RQ within a broader AI ethics philosophy that challenges instrumentalist norms and centers relational, reflexive responsibility.

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.

Modern datasets often consist of numerous samples with abundant features and associated timestamps. Analyzing such datasets to uncover underlying events typically requires complex statistical methods and substantial domain expertise. A notable example, and the primary data focus of this paper, is the global synthetic dataset from the Counter Trafficking Data Collaborative (CTDC) -- a global hub of human trafficking data containing over 200,000 anonymized records spanning from 2002 to 2022, with numerous categorical features for each record. In this paper, we propose a fast and scalable method for analyzing and extracting significant categorical feature interactions, and querying large language models (LLMs) to generate data-driven insights that explain these interactions. Our approach begins with a binarization step for categorical features using one-hot encoding, followed by the computation of graph covariance at each time. This graph covariance quantifies temporal changes in dependence structures within categorical data and is established as a consistent dependence measure under the Bernoulli distribution. We use this measure to identify significant feature pairs, such as those with the most frequent trends over time or those exhibiting sudden spikes in dependence at specific moments. These extracted feature pairs, along with their timestamps, are subsequently passed to an LLM tasked with generating potential explanations of the underlying events driving these dependence changes. The effectiveness of our method is demonstrated through extensive simulations, and its application to the CTDC dataset reveals meaningful feature pairs and potential data stories underlying the observed feature interactions.

Last week, we looked back at how 2024 made Elon Musk the world's most powerful man. Today, we're looking at a few other important themes that will influence the online and offline worlds in 2025. Google: Ruled an illegal monopoly in August, Google could be broken up. The results are anybody's guess, but what seemed impossible for a company worth 2.5tn is at play. The US has asked the judge in the case for a wholesale breakup of the giant, which would force it to divest Chrome, the world's most popular browser and one of Google's core businesses.

Human trafficking (HT) remains a critical issue, with traffickers increasingly leveraging online escort advertisements (ads) to advertise victims anonymously. Existing detection methods, including Authorship Attribution (AA), often center on text-based analyses and neglect the multimodal nature of online escort ads, which typically pair text with images. To address this gap, we introduce MATCHED, a multimodal dataset of 27,619 unique text descriptions and 55,115 unique images collected from the Backpage escort platform across seven U.S. cities in four geographical regions. Our study extensively benchmarks text-only, vision-only, and multimodal baselines for vendor identification and verification tasks, employing multitask (joint) training objectives that achieve superior classification and retrieval performance on in-distribution and out-of-distribution (OOD) datasets. Integrating multimodal features further enhances this performance, capturing complementary patterns across text and images. While text remains the dominant modality, visual data adds stylistic cues that enrich model performance. Moreover, text-image alignment strategies like CLIP and BLIP2 struggle due to low semantic overlap and vague connections between the modalities of escort ads, with end-to-end multimodal training proving more robust. Our findings emphasize the potential of multimodal AA (MAA) to combat HT, providing LEAs with robust tools to link ads and disrupt trafficking networks.

Wildlife trafficking (WLT) has emerged as a global issue, with traffickers expanding their operations from offline to online platforms, utilizing e-commerce websites and social networks to enhance their illicit trade. This paper addresses the challenge of detecting and recognizing wildlife product sales promotion behaviors in online social networks, a crucial aspect in combating these environmentally harmful activities. To counter these environmentally damaging illegal operations, in this research, we focus on wildlife product sales promotion behaviors in online social networks. Specifically, 1) A scalable dataset related to wildlife product trading is collected using a network-based approach. This dataset is labeled through a human-in-the-loop machine learning process, distinguishing positive class samples containing wildlife product selling posts and hard-negatives representing normal posts misclassified as potential WLT posts, subsequently corrected by human annotators. 2) We benchmark the machine learning results on the proposed dataset and build a practical framework that automatically identifies suspicious wildlife selling posts and accounts, sufficiently leveraging the multi-modal nature of online social networks. 3) This research delves into an in-depth analysis of trading posts, shedding light on the systematic and organized selling behaviors prevalent in the current landscape. We provide detailed insights into the nature of these behaviors, contributing valuable information for understanding and countering illegal wildlife product trading.

Trafficking of illicit firearms is one of the main sources of revenue for organized crime groups, while providing them the main tools for their antisocial trade: guns. Today's digital economy and breakneck technological progress have made it even more difficult for state authorities to properly control the situation. Law Enforcement Agencies (LEAs) face difficult challenges that were not even conceivable just a few years ago. For instance, criminals thrive in Dark Web marketplaces, which are hidden illegal e-shops that require specific URLs to access and constantly change locations to evade law enforcement. These platforms, along with cryptocurrency transactions, enable anonymous exchanges of illegal goods like firearms and drugs, complicating LEA efforts. Meanwhile, the Surface Web and social media are used by firearms traffickers for information exchange and coordination, where the vast volume of data hampers detection. Advances in 3D printing have fostered an on-line community sharing weapon blueprints, attracting criminal interest. Additionally, criminals exploit postal services to smuggle firearms, taking advantage of legal discrepancies between countries and the massive volume of daily parcels that conceal illicit goods. Lastly, identifying and matching seized firearms to international databases is challenging due to diverse weapon appearances, potential serial number tampering, and insufficient training among law enforcement practitioners.