By Stuart Kerr, Technology Correspondent
🗓️ Published: 12 July 2025 | 🔄 Last updated: 12 July 2025
📩 Contact: liveaiwire@gmail.com | 📣 Follow @LiveAIWire
🔗 Author Bio: https://www.liveaiwire.com/p/to-liveaiwire-where-artificial.html
Watching Without Disturbing
Deep in the forests of Borneo, camera traps blink silently. Above the savannahs of Kenya, drones hum through the sky. But behind the lens and rotor lies something invisible: artificial intelligence. From endangered pangolins to great whales, AI is now helping conservationists track, monitor, and protect wildlife in ways previously unimaginable.
Unlike traditional surveys that rely on manual tracking or satellite snapshots, AI-powered systems process massive amounts of image, video, and acoustic data in real time. They can identify species, flag poaching risks, and even predict migratory patterns. But while the tech is promising, it also raises questions about ethics, access, and effectiveness.
From Data to Decisions
As explored in AI in Agriculture, machine learning excels when paired with consistent, high-volume data. In conservation, this means wildlife cameras, environmental sensors, and community reporting systems.
The Conservation AI study (PDF) details how convolutional neural networks can detect over 10,000 species from camera trap images with near-human accuracy. In Hawaiʻi, the USGS has used AI to analyse the songs of native birds, helping protect habitats by detecting shifts in vocalisation patterns linked to environmental stress.
Meanwhile, NOAA Fisheries is using machine learning to monitor marine mammals and turtles, reducing human error in detection and response.
Eyes in the Sky, Algorithms on the Ground
AI is not just analysing images—it’s guiding real-time decisions. In Botswana, a U.S.-funded project is combining drones with AI to spot poachers before they strike. These autonomous systems relay data to rangers, who can act on alerts without endangering themselves.
But as we noted in AI in Disaster Response, speed must be matched by reliability. False positives could waste resources, while false negatives could cost lives—both human and animal.
Local Knowledge Meets Global Algorithms
While AI can see and hear at scale, it cannot understand cultural nuance or community context on its own. That’s why some of the most effective projects are hybrid. The USGS-Hawaiʻi initiative blends machine learning with Native Hawaiian ecological knowledge, strengthening conservation through trust and tradition.
This hybrid approach mirrors lessons from Invisible Infrastructure, where we saw that AI works best not in isolation, but in collaboration with human expertise.
Bias in the Wild
Despite progress, concerns remain. The IJCSPUB report (PDF) warns of "geographic data bias," where AI models perform poorly in regions with limited datasets. This could mean underprotection of species in the Global South, or missed anomalies in unfamiliar ecosystems.
As we’ve seen in AI in Space, training sets define what AI can detect—and what it overlooks. Without balanced data, even the best algorithms risk reinforcing ecological blind spots.
Nature, Augmented
AI is not a replacement for conservationists. It doesn’t replace boots on the ground, nor does it erase the need for funding, education, and policy reform. But it does offer something powerful: augmentation. With algorithms handling pattern recognition, field teams can spend more time acting on insights rather than searching for them.
The future of conservation may be less about choosing between technology and tradition, and more about designing systems where both can thrive.
About the Author
Stuart Kerr is the Technology Correspondent at LiveAIWire. He writes about AI’s impact on infrastructure, governance, creativity, and power.
📩 Contact: liveaiwire@gmail.com | 📣 @LiveAIWire
