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.

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.

In the fight against wildlife trafficking, Australian scientists are using the power of artificial intelligence. The method detects animals being smuggled in luggage or the mail using 3-Dimensional X-rays at airports and post offices, and algorithms then warn customs agents. This device uses artificial intelligence to recognize the morphologies of animals that are being trafficked. Australia has a diverse flora and fauna, which has supported an illicit wildlife trade. The researchers created a 3D-scanned "reference library" for three types of wildlife: lizards, birds, and fish, which they used to teach artificial intelligence algorithms to recognize the species.

A scanned lace monitor lizard (Varanus varius) image produced by using new technology. Blue-tongue lizards and sulphur-crested cockatoos are among the native animals frequently smuggled overseas. While the number of live animals seized by the Australian Government has tripled since 2017, the full scale of the problem eludes us as authorities don't often know where and how wildlife is trafficked. Now, we can add a new technology to Australia's arsenal against this cruel and inhumane industry. Our research shows the potential for new technology to detect illegal wildlife in luggage or mail.

Blue-tongue lizards and sulfur-crested cockatoos are among the native animals frequently smuggled overseas. While the number of live animals seized by the Australian Government has tripled since 2017, the full scale of the problem eludes us as authorities don't often know where and how wildlife is trafficked. Now, we can add a new technology to Australia's arsenal against this cruel and inhumane industry. Our research, published in Frontiers in Conservation Science today, shows the potential for new technology to detect illegal wildlife in luggage or mail. This technology uses artificial intelligence to recognize the shapes of animals when scanned at international frontlines such as airports and mail centers.

Strategically placed recycled cell phones are combating deforestation by sending notifications to rangers when chainsaw noises are recorded. Algorithms similar to those used by Homeland Security are being developed to provide effective routes for ranger patrols in their battle against poaching. And drones are delivering sylvatic plague vaccines to prairie dog populations in an effort to save the Black Footed Ferret, a highly endangered predator of prairie dogs, from extinction. What happens when wildlife biologists join forces with computer scientists? A new era in wildlife conservation is born!

Remember that endangered anteater-esque ball of scales from Favreau's recent film, The Jungle Book? Over one million pangolins have likely been poached and illegally traded in the last decade, especially because of their importance in Chinese medicine and food. And the internet hasn't exactly helped its plight. As wildlife trade monitoring network TRAFFIC reports, their researchers investigated 39 Chinese e-commerce websites over June and July 2016; a single survey in the first month alone detected 153 ads for pangolin scales and meat and live specimen from 94 traders across six sites. Virtual wildlife trafficking poses a serious threat to not just the pangolin.