Nature Meets the Algorithm: How AI Could Safeguard (or Endanger) East Africa’s Biodiversity

Giraffes in Murchison Falls National Park, Uganda (Photo: Kimbowa R)

Technology, such as remote sensing and data analytics, enhances real-time monitoring, enabling proactive conservation efforts. This ensures the protection of diverse ecosystems, preserves critical habitats, and mitigates threats, ultimately leading to the sustained health of ecosystems and the preservation of endangered species (UNSSC & UNEP, 2025).

Opportunities to address biodiversity loss (one of the planetary crises of global concern) have gained traction, leading to the recently agreed Kunming-Montreal Global Biodiversity Framework. The Framework seeks to respond to the Global Assessment Report of Biodiversity and Ecosystem Services issued by the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (2019), the fifth edition of the Global Biodiversity Outlook (2020), and many other scientific documents that provide ample evidence that, despite ongoing efforts, biodiversity is deteriorating worldwide at rates unprecedented in human history. 

Another opportunity lies in artificial intelligence (AI) and digital transformation, with the potential to support the protection and sustainability of the world’s rapidly disappearing rich variety of life forms. However, this prospect is now a matter of political and public policy debate, as there are critical questions around its application as an aid to reverse the loss of wildlife and its habitat (biodiversity) in a benevolent way.

For example, in East Africa, as Partner States’ biodiversity-related policies are gradually revised, there are possibilities to consider AI and digital transformation. A case in point is Uganda’s Wildlife Policy (2014) that is under review. 

East Africa’s savannahs, forests, and wetlands are home to some of the planet’s most remarkable life forms — from elephants roaming the Serengeti to coral reefs off Zanzibar’s shores. Yet, as pressures from climate change, deforestation, and poaching intensify, the region faces a sobering question: can artificial intelligence (AI) and digital innovation help protect this natural wealth, or will they introduce new risks of their own?

The Promise: Giving Nature New Eyes

In Kenya’s rhino sanctuaries, AI-powered thermal cameras now detect human movement at night and alert rangers in real time. These systems, developed through collaborations between conservation agencies and tech partners, have significantly reduced poaching incidents by enabling quicker, data-driven responses (Wachiuri et al., 2023). Similarly, Wildlife Insights, a global platform supported by Conservation International, uses AI to analyse millions of camera-trap images — automatically identifying species and sharing standardised biodiversity data across research networks. For resource-strapped conservation teams in East Africa, such efficiencies are revolutionary.

Beyond ground-based systems, drones and satellite imagery are reshaping ecosystem monitoring. In Uganda and Tanzania, pilot projects now deploy drones equipped with machine learning algorithms to map wildlife movements, illegal logging, and habitat changes (Kariuki & Mugo, 2022). These AI-enhanced tools extend the reach of rangers and allow continuous surveillance of vast terrains that would otherwise remain unmonitored.

At a broader scale, digital early-warning systems like Global Forest Watch (GFW) employ machine learning to detect tree-cover loss from space and deliver near-real-time alerts (Hansen et al., 2013). Public Global Forest Change data from Hansen et al. (2013) is available through GFW. Forest departments and local communities can then act before deforestation accelerates. In places like Mount Elgon and the Mau Forest, where encroachment has long been a challenge, such predictive systems are giving policymakers a fighting chance. 

The Perils: When Algorithms Get It Wrong

But for every success story, there’s a cautionary tale. Machine learning models are only as good as the data they’re trained on. When algorithms misclassify animals or humans — say, mistaking a ranger for a poacher — it wastes resources and can even put lives at risk. Many of these systems rely on training data from outside Africa, leading to errors in low-light conditions or in vegetation-dense landscapes. Due to accuracy concerns, GFW datasets such as the Hansen data may be used to offer an initial understanding of global, national, or regional trends in forest cover or identify large-scale forest loss events in certain regions, but should not be the primary source for business-sensitive analyses, such as ensuring compliance with the EU Deforestation Regulation (C. Busse, 2024). 

There’s also a social risk. Conservation technology often involves surveillance — drones, cameras, and sensors that capture not only wildlife movements but also human activity. Without strong governance, such tools could be misused to monitor or restrict local communities, undermining trust and exacerbating inequality (Büscher & Fletcher, 2020).

Moreover, digital conservation can unintentionally sideline the very communities that have been stewards of these ecosystems for generations. If AI replaces rather than empowers indigenous and local knowledge systems, it risks eroding cultural ties to the land.

The Path Forward: Blending Tech with Tradition

Harnessing AI for biodiversity protection doesn’t mean handing over control to machines. The key lies in co-designing solutions with local people, validating AI models on local data, and ensuring that the resulting insights remain open and accessible. Projects in South Africa and Kenya are already experimenting with community-led data collection and digital governance frameworks that prioritise transparency and shared ownership (Mogomotsi & Kefasi, 2021).

AI should not replace human intelligence but rather amplify it — helping rangers, scientists, and communities act faster and smarter in protecting what they already value deeply.

If East Africa gets this balance right, the fusion of AI and local stewardship could mark a new chapter in conservation — one where algorithms don’t dominate nature, but defend it.

References

• C. Busse (2024). Why Global Forest Watch Is Not Suited for Ensuring EUDR Compliance https://www.nadar.earth/media/why-gfw-is-not-suited-for-eudr-compliance (July 2024).
• Hansen, M. C. et al. (2013). High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342(6160), 850–853.
• Kariuki, J., & Mugo, R. (2022). Drone Technology and AI Applications in Wildlife Conservation in East Africa. African Journal of Environmental Science, 18(2), 45–59.
• Wachiuri, E., et al. (2023). Leveraging Artificial Intelligence for Rhino Protection in Kenya’s Conservancies. Conservation Science and Practice, 5(4).
• Büscher, B., & Fletcher, R. (2020). The Conservation Revolution: Radical Ideas for Saving Nature Beyond the Anthropocene. Verso.
• Mogomotsi, P., & Kefasi, N. (2021). Digital Conservation Governance in Southern Africa: Balancing Innovation with Inclusion. Development Southern Africa, 38(6), 789–804.
• UNSSC & UNEP. Module 3: Digital Sustainability for Biodiversity Action. The Digital4Sustainability Learning Path