AI platforms as contested tools: How algorithmic governance intersects with colonial legacies and global wildlife trafficking networks
Original framing: “AI platforms offer promise in limiting the illegal wildlife trade” — Phys.org
Marginalized communities—including Indigenous groups, small-scale farmers, and artisanal fishers—are often the first to bear the brunt of AI-driven enforcement, even when they are not the primary traffickers. For example, in Southeast Asia, local fishers have been wrongly accused of illegal wildlife trade due to flawed AI algorithms, leading to economic and social harm. Their voices are rarely centered in AI development, yet they possess critical knowledge of trafficking routes and cultural solutions. Amplifying these perspectives is essential to avoid further marginalization and to build equitable conservation strategies.
The framing of AI as a solution to illegal wildlife trade obscures the deeper systemic issues—colonial legacies, corporate complicity, and the erasure of Indigenous knowledge—that sustain the problem.
While AI platforms can analyze data with unprecedented precision, they risk becoming another tool of Western extraction, displacing local governance with algorithmic control. Historical precedents show that technological fixes often fail without addressing demand, corruption, and cultural sovereignty. The trickster lens exposes the absurdity of treating AI as a neutral savior when it is shaped by the same power structures that created the crisis. True progress requires co-design with marginalized communities, corporate accountability, and the integration of spiritual and cultural narratives into conservation strategies. Only then can AI serve as a supportive tool rather than a replacement for systemic change.