If you’ve spent any time this year around a news source (or your favorite newsletter), you're no stranger to the fact that bear maulings seem to be on the rise - and not just here in America. With stories making headlines from far-off places like Japan (who has enlisted their military for help), British Columbia (where 11 children were attacked during a school picnic) and right here at home with a pair of rare attacks in Arkansas and the Sunshine State’s first-ever recorded bear fatality, among many others in 2025 alone.
The truth is, bear populations are on the rise and as we continue to encroach and recreate in and around their habitat, conflicts are bound to happen.
Traditional responses to these events often result in fast-paced hunts led by wildlife resource agencies and even sometimes large-scale culls to attempt to quickly locate and dispatch ‘problem’ bears. The issue with these strategies is that they often prove ineffective or imprecise, sometimes harming non-problem bears while failing to address repeat offenders. This is exactly where scientists and researchers with the BearID Project are hoping to intervene. At the heart of the project is an open-source initiative developing AI-powered facial recognition software tailored for bears. While primarily designed for noninvasive conservation monitoring, the technology is gaining attention for its potential to reduce conflicts by enabling faster, more targeted wildlife management.
“We’re trying to give people tools to prevent conflicts,” Elbert Bakker, a research support specialist for Polar Bears International said. “There’s lots of potential for this technology.”
The tech in use | BearID Project
BearID uses deep learning neural networks, adapted from human facial recognition algorithms, to process images and videos from remote camera traps. The system first detects a bear's face, rotates and extracts it, then measures unique facial features (such as distances between eyes, nose, ears, and other landmarks). These measurements create a digital "embedding" or signature for each bear, which is matched against a database to assign an individual ID, even for bears lacking obvious natural markings like scars or ear tags.
Early tests on brown/grizzly bears achieved around 84% accuracy in controlled settings, with ongoing refinements using more diverse training data to boost performance. The software is designed to be freely available, adaptable to other Ursidae species, and integrable with existing tools like camera networks or drones for real-time or near-real-time analysis.
The project is non-invasive in nature, involving no trapping, collaring, or DNA sampling. Instead, bears are monitored passively as they move through their habitats, providing rich data on movements, social interactions, health, and population trends without stressing the animals.
The mission is to advance these types of monitoring techniques for better population estimates, behavioral ecology studies, and evidence-based conservation. By automating identification, BearID saves researchers countless hours of manual review and enables scalable surveys across large landscapes. The project has expanded internationally, including collaborations in Ecuador to apply similar AI to Andean (spectacled) bears and camera trap analysis.
An Andean bear identified | BearID Project
And while it was not originally built as an attack-prevention system, BearID's speed and precision offer promising applications for reducing risks. In conflict zones, wildlife managers often struggle to link specific bears to incidents, relying on slow, expensive DNA analysis from saliva, hair, or wounds. BearID could change that by rapidly matching post-incident photos, drone footage, or nearby camera trap images to known individuals.
Experts suggest integrating BearID with drone surveys or alert networks to flag repeat "problem" bears near communities, allowing proactive interventions like hazing, food-source removal, or focused relocation, all of which could be potentially more effective than indiscriminate hunting.
In a climate that is seemingly warming up to the idea of legally hunting large species like grizzly bears again here in the Lower 48, this in tandem with regulated hunts might be a great combination of initiatives that will serve to curb attacks.
At this point, BearID simply demonstrates how AI can bridge conservation science and practical risk reduction, only time will tell if it will serve to help mitigate conflicts and thus, attacks, in shared areas.
