AI-based Strategies to Combat Wildlife Trafficking and Wet Markets in Asia: A Critical Review

By Natasha Rusch and Payal Arora

Key Takeaways

  • We face an uphill battle against the illicit wildlife poaching industry given its estimated value of US$7–23 billion. It remains an attractive market as it is regarded as a high-profit, low-risk business. Artificial Intelligence (AI) based approaches promise to flip the script and make it a high-risk pursuit.
  • To combat wildlife crime, the World Wildlife Fund’s (WWF) programme identified four core pillars — stop the poaching, stop the trafficking, stop the buying, and international policy. For AI oriented strategies to be successful, they need to intervene at all four levels.
  • Given that half of the world’s wildlife poaching takes place in Africa, and that this continent is plunging into recession due to the pandemic, the illicit wildlife trade is expected to gain strides. This has renewed the urgency for innovative AIbased solutions and fostered partnerships between global technology companies and conservation organisations to rise to this challenge.
  • This study maps the current AI-based challenges, initiatives, and voices from stakeholders, and captures insights to important questions such as to what extent can/does AI mitigate illegal wildlife trafficking problems? What are the different beliefs among stakeholders about wildlife poaching and online trafficking and why? How is AI being embedded in these initiatives?
  • There is a bias towards market-based solutions among the African stakeholders at a time when the international funding sectors in Europe are going against such measures. Cultural perspectives matter in AI-led enforcement, which demands local buy-in.
  • Exorbitant costs to sustain AI interventions sit uncomfortably with major resource scarcities in pay for the rangers and their informant networks on the ground, still seen by conservationists as the most “intelligent” way to combat the trade.
  • Anti-poaching tracking initiatives need to address ongoing dilemmas of data governance such as data cooperation vs data localisation/ownership, and open science vs privacy/security to have real impact.

TABLE 1

While much research in this area of conservation focuses either on AI-led anti-poaching measures to check supply or policies to deter demand, few studies emphasise the global supply chains that intersect transnational actors, in this case Africa and Asia. The difficulties in creating AI applications for online trafficking are to a certain extent due to different stakeholder interests driving the nature conservation field and the technology industry. Therefore, it is essential to explore the diverse aspects of these socio-technical systems to address the significant and global challenges in eliminating wildlife poaching and trade, particularly of endangered species.

Dev is a Fellow at Digital Asia Hub and Yenching Scholar at Peking University, currently conducting research on the use of tech in the Social Credit System.