Takeaways
- Conservation decisions are often made under uncertainty, and AI models can help optimize actions even with limited or noisy data.
- The urgency of biodiversity loss means that waiting for perfect information can be more harmful than making decisions with uncertainty-aware models.
- Decision AI needs to be interpretable and transparent to be adopted in conservation, where trust and human understanding are critical.
- AI for conservation must shift from isolated applications to globally coordinated frameworks aligned with biodiversity policy targets.
- The value of data varies, and AI can help prioritize which data to collect in resource-constrained conservation environments.
Summary
Iadine Chades presents how decision-oriented artificial intelligence can address the accelerating biodiversity crisis, in which 25 percent of species face extinction and global species abundance has declined by 70 percent over fifty years. She distinguishes between perception AI—used for interpreting data through image recognition or natural language processing—and control AI, which supports decision making and planning under uncertainty. The heart of her work lies in applying Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs) to conservation problems, enabling the identification of optimal, sequential actions under limited budgets, dynamic ecosystems, and imperfect data.
Through a case study on British Columbia’s northern abalone and sea otters, Chades illustrates how MDPs model species interactions, management actions (such as anti-poaching or species introduction), and stochastic dynamics (including oil spills and illegal fishing). Simulations revealed that even optimal strategies may fail to achieve dual recovery targets, prompting managers to rethink assumptions and explore novel interventions. To enhance interpretability and uptake among practitioners, her team developed K-state MDPs, which compact decision trees to a user-specified number of states, and factored MDPs, which exploit causal structure for efficient representation. Both approaches produce transparent rules that guide conservation teams without overwhelming complexity.
Chades then addresses POMDPs, which account for imperfect detection of rare species and enable managers to decide when to shift resources between monitoring and active management. She demonstrates that POMDP-based adaptive management yields optimal policies even when future dynamics are uncertain, as in climate-impacted shorebird conservation. Across all methods, co-design with local agencies ensures that models reflect ground realities and earn stakeholder trust. Chades emphasizes that minimal, interpretable models often perform nearly as well as full-scale formulations and are more readily adopted by practitioners.
Looking ahead, she advocates for a paradigm shift toward a globally coordinated AI strategy aligned with the Kunming-Montreal Global Biodiversity Framework, enabling shared priorities, data standards, and open tools. She sees emerging opportunities in generative AI—such as synthesizing gray literature, extracting causal relationships, and automatically translating stakeholder objectives into decision models—but cautions that robust safeguards and interdisciplinary collaboration are essential. Chades concludes by calling for sustained investment in co-designed, explainable decision AI to help reverse the biodiversity decline with optimal use of scarce resources.
