Japanese and South Korean tech stocks tumbled Friday, with Softbank falling over 10% amid AI bubble concerns. HONG KONG - Japanese and South Korean tech stocks plummeted on Friday, with tech investor SoftBank plunging more than 10% as fears over an AI bubble weighed on the market. The selling followed a downbeat session on Wall Street after U.S. jobs data clouded hopes of further interest rate cuts and fears about whether red-hot valuations for artificial intelligence shares are justified. Seoul's benchmark Kospi index was trading down nearly 4%, while Tokyo's Nikkei index shed 2.3% in morning trade. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. At an adult industry conference in Prague last month, delegates noted a sharp increase in sites offering users the chance to form AI relationships. 'Obedient, yielding and happy to follow': the troubling rise of AI girlfriends E leanor, 24, is a Polish historian and lecturer at a university in Warsaw; Isabelle, 25, is a detective serving with the NYPD; Brooke, 39, is an American housewife who enjoys an opulent Miami lifestyle financed by her frequently absent husband. All three women will flirt and chat and send nude photographs and explicit videos via one of a soaring number of new adult dating websites that offer an increasingly realistic selection of AI girlfriends for subscribers willing to pay a monthly fee. At the TES adult industry conference in Prague last month, delegates noted a sharp increase in new websites offering users the chance to form relationships with AI-generated girlfriends, who will remove their clothes in exchange for tokens purchased by bank transfer.

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.

Our study addresses the challenges of building datasets to understand the risks associated with organized activities and human trafficking through commercial sex advertisements. These challenges include data scarcity, rapid obsolescence, and privacy concerns. Traditional approaches, which are not automated and are difficult to reproduce, fall short in addressing these issues. We have developed a reproducible and automated methodology to analyze five million advertisements. In the process, we identified further challenges in dataset creation within this sensitive domain. This paper presents a streamlined methodology to assist researchers Figure 1: Methodology to generate a pseudo-labeled in constructing effective datasets for combating dataset in human trafficking risk prediction and organized organized crime, allowing them to focus on activity detection tasks.

The task of multimedia geolocation is becoming an increasingly essential component of the digital forensics toolkit to effectively combat human trafficking, child sexual exploitation, and other illegal acts. Typically, metadata-based geolocation information is stripped when multimedia content is shared via instant messaging and social media. The intricacy of geolocating, geotagging, or finding geographical clues in this content is often overly burdensome for investigators. Recent research has shown that contemporary advancements in artificial intelligence, specifically computer vision and deep learning, show significant promise towards expediting the multimedia geolocation task. This systematic literature review thoroughly examines the state-of-the-art leveraging computer vision techniques for multimedia geolocation and assesses their potential to expedite human trafficking investigation. This includes a comprehensive overview of the application of computer vision-based approaches to multimedia geolocation, identifies their applicability in combating human trafficking, and highlights the potential implications of enhanced multimedia geolocation for prosecuting human trafficking. 123 articles inform this systematic literature review. The findings suggest numerous potential paths for future impactful research on the subject.

This project tackles the pressing issue of human trafficking in online C2C marketplaces through advanced Natural Language Processing (NLP) techniques. We introduce a novel methodology for generating pseudo-labeled datasets with minimal supervision, serving as a rich resource for training state-of-the-art NLP models. Focusing on tasks like Human Trafficking Risk Prediction (HTRP) and Organized Activity Detection (OAD), we employ cutting-edge Transformer models for analysis. A key contribution is the implementation of an interpretability framework using Integrated Gradients, providing explainable insights crucial for law enforcement. This work not only fills a critical gap in the literature but also offers a scalable, machine learning-driven approach to combat human exploitation online. It serves as a foundation for future research and practical applications, emphasizing the role of machine learning in addressing complex social issues.

With the advancement of technology, Artificial Intelligence (AI) is increasingly being used to aid in crime solving. AI has the potential to be used in all aspects of crime solving, from aiding in investigations to helping in the prosecution of suspects. AI can be used to analyze huge amounts of data quickly and accurately, allowing investigators to make faster, more informed decisions. AI can also help in facial recognition and biometric identification, allowing for more accurate identification of suspects and witnesses. By utilizing AI in crime solving, law enforcement can more effectively and efficiently combat crime.

Position: Social Science Researcher, Senior Manager Department: Learning, Innovation, and Data Systems FLSA Status: Full-Time, Exempt Reports to: Director, Learning, Innovation and Data Systems Direct Reports: None Date Issued: October 2022 Date Revised: N/A Location: Washington, DC The Mission Polaris is leading a data-driven social justice movement to fight sex and labor trafficking at the massive scale of the problem – 25 million people worldwide deprived of the freedom to choose how they live and work. For more than a decade, Polaris has assisted thousands of victims and survivors through the U.S. National Human Trafficking Hotline, helped ensure countless traffickers were held accountable and built the largest known U.S. data set on actual trafficking experiences. With the guidance of survivors, we use that data to improve the way trafficking is identified, how victims and survivors are assisted, and how communities, businesses and governments can prevent human trafficking by transforming the underlying inequities and oppression that make it possible. The Learning, Innovation, and Data Systems team has the exciting task of utilizing research and data to inform and guide our approach to the fight against human trafficking with the ultimate end goal of eradicating the crime of modern-day slavery. About Opportunity The Social Science Researcher is a highly self-motivated, creative, and methodical professional.

ABSTRACT Human trafficking is a universal problem, persistent despite numerous efforts to combat globally. Individuals of any age, race, ethnicity, sex, gender identity, sexual orientation, nationality, immigration status, cultural background, religion, socio-economic class, and education can be a victim of human trafficking. With the advancements in technology and the introduction of autonomous vehicles (AVs), human traffickers will adopt new ways to transport victims, which could accelerate the growth of organized human trafficking networks, whcih can make detection of trafficking in persons more challenging for law enforcement agencies. The objective of this study is to develop an innovative audio analytics-based human trafficking detection framework for autonomous vehicles. The primary contributions of this study are to: (i) define four non-trivial, feasible, and realistic human trafficking scenarios for AVs; (ii) create a new and comprehensive audio dataset related to human trafficking with five classes--i.e., crying, screaming, car door banging, car noise, and conversation; and (iii) develop a deep 1-D Convolution Neural Network (CNN) architecture for audio data classification related to human trafficking. We have also conducted a case study using the new audio dataset and evaluate the audio classification performance of the deep 1-D CNN. Our analyses reveal that the deep 1-D CNN can distinguish sound coming from a human trafficking victim from a non-human trafficking sound with an accuracy of 95%, which proves the efficacy of our framework. INTRODUCTION Human trafficking is a global epidemic. People of any age, gender identities, and ethnicities from all across the world are constantly under threat of being victim of human trafficking. According to the Department of Homeland Security, falsification or threat of force is used to acquire cheap labor or commercial sex acts in human trafficking (1).