D-ISN: TRACK 2: Disrupting Wildlife Trafficking Networks through Convergence of Physical and Virtual Ecosystems

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Wildlife trafficking occurs in both physical and virtual (both open and dark) crime ecosystems; its illicitness is typically masked by the legal wildlife trade. The security and economic impacts of unfettered wildlife trafficking are existential threats to the U.S. To begin to address these impacts, information gathering and analysis are needed; for example, machine learning tools to aid discovery and help trace the status and trends of illegal goods could significantly support law enforcement at U.S. Ports of entry. The promise of inferences to bolster the disruption of wildlife trafficking networks depends on a scientific community with capacity to distinguish 1) legal from illegal trade; 2) the financial, and 3) flows of other illicit goods within and across crime ecosystems. This research converges engineering, computer and data science, and social science in a deliberate fashion to improve understanding of illicit supply network operations and strengthen ability to detect, disrupt and dismantle them. This proposal integrates operational, computational, financial, social, cultural, and economic expertise to build new research capacity to: 1) identify analytically relevant data; 2) leverage united data and predictive methods to draw associations and make inferences about interventions to combat wildlife trafficking; 3) expand the research community by suggesting novel research problems and directions, engaging civil society, federal agencies, and private or non-profit entities; and 4) crystalize research questions for the future. Although the team will focus on wildlife, the applicable methodology and research questions are transferable to other problems such as human trafficking. The project’s four-phase research approach: 1) distills relevant analytic parts of the problem to diverse experts; 2) initiates team and research capacity building activities to enable analytically relevant unification of data; 3) implements activities to collate, organize, and identify ways to analyze collected data through three strategic face-to-face meetings, world cafés, a literature review and informational interviews; and 4) catalyzes research questions through a wrap-up brainstorming session. Central to this proposal are two undergraduate interdisciplinary team-projects directly responding to needs identified by the anti-wildlife trafficking community and lying at the intersection of science and society. The convergence of expertise in environmental crimes, computer science, conservation biology, operations modeling and analytics contributes to advancing knowledge in at least three fundamental ways by: 1) understanding the landscape of physical and virtual criminal ecosystems; 2) assessing data, technical and scientific needs associated with linking the ecosystems, and 3) developing a strategy to deploy intelligent techniques (e.g., information retrieval, analytics, AI and engineering) to characterize and disrupt wildlife trafficking networks. Three strategy meetings will generate in-depth discussion among experts from various fields (e.g., social science, computer science, data science, engineering) and organizations (e.g., parastatals, foundations, civil society organizations, universities, private sector industries and government agencies), and open new research directions and questions, illustrating the relevance of science for disrupting wildlife trafficking networks to our research community. Future research agendas may enhance discovery of other illicit supply chain activities that help meet national security, law enforcement and economic development needs and policies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.