TL;DR:
- The illegal wildlife trade poses a significant challenge, fueled by a multi-billion-dollar black market and evolving tactics.
- AI is being harnessed to combat this trade through various applications.
- AI-powered cameras and sensors are used to monitor and track wildlife, alerting authorities to poaching activities.
- AI algorithms assist in identifying illegal wildlife products and analyzing DNA for species identification.
- Tools powered by AI monitor social media and e-commerce platforms to detect illegal trade activities.
- Ethical and political concerns regarding data privacy and funding limitations need to be addressed.
Main AI News:
The remarkable advancements in artificial intelligence (AI) during the first quarter of 2023 have left the world in awe and anticipation of an imminent technological revolution. From revolutionizing medical imaging and autonomous navigation to dominating strategic board games like Go and transforming education and industry through sophisticated language analysis and synthesis, AI models have proven their prowess. Now, the question arises: how can AI be harnessed to confront complex and persistent challenges such as the illegal wildlife trade?
In a recent incident at Hai Phong port, Vietnam’s bustling cargo hub, customs officials made a startling discovery. What was declared as a shipment of ordinary peanuts turned out to be a 6-meter-long metallic container filled not with peanuts but with hundreds of severed ivory tusks stacked together. This marked the third interception of illegal ivory at the port this year, with this particular haul weighing over 7,000 kilograms—the largest seizure ever recorded at Hai Phong. The exorbitant price of raw ivory, ranging from US$100 to $2,500 per kilogram, rivals that of cocaine.
However, ivory is just the tip of the iceberg. The ban on the international trade of elephants and rhinos, along with their parts, not only failed to curb trafficking but also led to the exploitation of other vulnerable species. Helmeted hornbills now face the threat of extinction as their red keratin casques are marketed as a “new” form of ivory. Similarly, the pearl-tinted bivalve shells of giant clams have become targets, further endangering their existence. This illicit global trade extends beyond ivory, encompassing thousands of species, including large mammals, plants, and fungi, exploited for traditional medicines, exotic pets, and fashion. This rampant black market is estimated to be worth up to US$20 billion annually by the United Nations Environment Programme and INTERPOL. Yet, the true magnitude of this illicit trade remains elusive due to its clandestine nature.
The illegal wildlife trade operates through a network of three primary phases: collection and harvest in source countries, trafficking networks for processing and transportation, and sale and purchase in destination countries. At each stage, intricate tactics are employed to evade detection and capture. In the case of the Hai Phong seizure, it was discovered that the contraband had been transshipped in Singapore, employing deceptive language and inaccurate information in the declaration to obfuscate its origin and routes.
The challenge faced by customs officials is compounded by the difficulty of identifying species and their origins. Determining whether a transboundary species was legally or illegally harvested, as well as identifying the country of origin, is an arduous task. Moreover, distinguishing unattached shark fins and identifying the species used in products such as trinkets, garments, medicines, or powders requires considerable manpower, expertise, and keen observation. The rise of online platforms for illegal product sales further complicates law enforcement efforts, as sellers resort to code names, misspellings, and frequent platform hopping to conceal their identities and locations. The methods employed by these illicit networks are in a constant state of evolution and adaptation.
The conservation community sees a glimmer of hope in the potential of AI. As algorithms continue to advance and AI becomes more integrated into everyday life, conservationists aspire to leverage this technology to combat the illegal wildlife trade. Professor Payal Arora, a digital anthropologist and Erasmus University Rotterdam professor focused on technology and social issues, highlights the potential for AI to automatically monitor vast amounts of online data, effectively preventing and disrupting illegal trade.
The intersection between AI and the fight against the illegal wildlife trade is becoming increasingly evident. AI-powered cameras and sensors are being deployed to monitor wildlife and track various species in their natural habitats. Archangel Imaging, a UK-based start-up, combines AI with cameras, motion detectors, and satellite communications to develop the Argonaut. This innovative camera alerts nearby rangers of poaching activities, eliminating the need for daily foot patrols. The University of Southern California has developed the Protection Assistant for Wildlife Security (PAWS), an AI-powered tool that utilizes game theory, historical activity data, and topographical information to predict poaching hotspots and optimize patrol routes.
Moving further along the supply chain, AI algorithms have been trained to identify illegal wildlife products and aid forensic scientists in DNA analysis. This facilitates the detection of illegal products at border checkpoints and assists in determining the species and origin of the seized items. The iSharkFin software, developed by the United Nations Food and Agriculture Organization (FAO) and the University of Vigo, uses fin shape analysis to identify shark species. Additionally, a collaborative effort between Conservation International, the Singapore National Parks Board, Microsoft, and other partners resulted in the creation of the Fin Finder app, which can quickly identify shark species. AI-powered tools are also employed to monitor social media and e-commerce platforms, utilizing natural language processing and image recognition technology to detect keywords and images associated with illegal wildlife trade activities.
However, while AI shows immense promise, its nascent stage raises ethical and political concerns, particularly regarding data privacy and open source access. These dilemmas are further exacerbated within the complex web of actors and activities involved in the illegal wildlife trade. Professor Arora cautions against limiting the potential of AI due to funding constraints, emphasizing the importance of robust investment in intelligence gathering. The costs associated with implementing AI solutions should not overshadow the pressing resource scarcity at play.
Conclusion:
The integration of AI into the battle against illegal wildlife trade presents significant opportunities for tackling this global issue. With the use of AI-powered cameras, sensors, and algorithms, authorities can enhance their monitoring and surveillance capabilities, effectively disrupting poaching activities. The ability to identify and analyze illegal wildlife products using AI technology aids in the detection and prevention of trafficking. Furthermore, AI tools that monitor online platforms contribute to the proactive identification of illegal trade activities. However, careful consideration must be given to ethical concerns, data privacy, and the allocation of adequate funding to fully leverage the potential of AI in this market.
