The Promise and Pitfalls of AI in Conservation: Experts Map Future Possibilities

A new paper, The potential for AI to revolutionize conservation: a horizon scan, published in Trends in Ecology & Evolution, has identified 21 promising ways artificial intelligence could revolutionize conservation efforts. An international panel of 27 experts, including both conservation scientists and AI specialists, evaluated over 100 potential applications before identifying the most impactful opportunities.

Key applications highlighted in the study include:

• Using AI to detect new species and uncover "dark diversity" through automated analysis of images

• Developing digital twins of ecosystems to better predict outcomes of conservation interventions

• Improving species distribution models by integrating multiple data sources

• Creating AI-powered conservation advisors that can synthesize evidence and provide context-specific recommendations

• Monitoring online wildlife trade through computer vision and natural language processing

• Using distributed acoustic sensing to transform monitoring of marine species

While highlighting AI's transformative potential, the researchers emphasize it should complement rather than replace traditional conservation approaches. They also outline important challenges to address, including:

• Ensuring equitable access to AI tools between Global North and South

• Preventing "AI colonialism" where data from the Global South is extracted to train models in the Global North

• Maintaining essential on-the-ground conservation skills

• Protecting against potential misuse of monitoring technologies

The study provides a framework for how the conservation field can adapt to harness AI's benefits while mitigating risks, emphasizing the importance of interdisciplinary collaboration and local knowledge integration.

Research Reference: Sam A. Reynolds, Sara Beery, Neil Burgess, Mark Burgman et al. “The potential for AI to revolutionize conservation: a horizon scan” Trends in Ecology & Evolution, December 17, 2024. DOI: 10.1016/j.tree.2024.11.013