AI Helps Uncover Hidden Airport Hotspots for Illegal Wildlife Trafficking

New research from a team including a conservation criminologist at the University of Maryland and computer scientists from the University of Southern California demonstrates how artificial intelligence (AI) and network science can help authorities and conservation organizations combat the illegal wildlife trade.

In a study published in May in Nature Communications Earth & Environment, the researchers identified 307 airports around the world that could serve as trafficking hubs, despite having no recorded seizures. That finding was based on their predictive model’s analysis of the characteristics of almost 2,000 airports, and included patterns in historical trafficking data as well as current insights about key airport features, such as centrality within flight networks.

Of those, 11 emerged as high-confidence “hidden hotspots,” including two U.S. airports, Dallas Fort Worth International and Denver International, that had not been previously flagged in global trafficking databases.

The incidence of flora-related crimes—trafficking in restricted plants—at an airport, along with the strength of local counter-trafficking or law enforcement resilience measures, also emerged as significant predictors.

Catching traffickers red-handed can be exceedingly difficult for law enforcement agencies because of the sophisticated methods criminal networks employ.

“Authorities and conservation actors have been frustratingly stuck reacting to offenders who are constantly innovating,” said Professor Meredith Gore, research director in UMD’s Department of Geographical Sciences and a co-author of the study. “This research leapfrogs over these challenges using the best available open data, exposing previously entombed information about the illegal wildlife trade in the global airline network and laying a soundtrack for a substantial advancement in computational approaches with regard to conservation criminology and team science.”

In addition to the U.S. airports, other hotspots were identified in China, Indonesia, Italy, Mexico and the Philippines. The study identified Africa as the dominant source of illegally traded wildlife, and Asia as the dominant destination.

“These findings can empower decision-makers to make more proactive choices on how to prevent wildlife trafficking, rather than the current reactive approaches,” said the paper’s lead author, Hannah Murray, a USC doctoral student in computer science and a student leader at the USC Center for AI in Society (CAIS).

“The most significant outcome of our model is the practical insights it offers to those invested in combating the illegal wildlife trade, such as how to allocate limited resources and prioritize where interventions are needed to make the most impact,” Murray said.

The illegal taking, trading and transporting of wild animals, plants and their products is a major driver of biodiversity loss, but efforts to curb this activity remain underdeveloped, said USC computer science Associate Professor Bistra Dilkina, co-director of CAIS, the paper’s other co-author.

“Illegal wildlife trade is the second-biggest threat to wildlife after habitat loss and fragmentation, and we urgently need to address it more effectively in order to preserve key biodiversity,” she said. “Yet the knowledge and data-driven tools available for the fight against wildlife trafficking are limited. It doesn’t have to be this way.”

Armed with new information from AI approaches that bring invisible trafficking patterns to light, customs authorities could consider starting or increasing cargo and hand-luggage screening at the newly-flagged airports.

“Airlines operating at those locations might also require crew members to complete annual wildlife trafficking awareness training, such as those offered by the International Air Transport Association,” said Gore. “In parallel, the conservation community could step up engagement with the airline and passenger transport sectors in Oceania to support awareness-building, monitoring and improved data collection.”

This article was based on a story by Greg Hardesty and Caitlin Dawson of USC.