Reconciling top-down conservation priorities with bottom-up local needs
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Abstract
The success of global conservation goals risks being undermined by conflicts that arise when high-level, data-driven priorities clash with local needs and contexts. While top-down systematic planning efficiently identifies priority areas using large-scale, multi-dimensional data, it neglects the input of local communities and stakeholders. Here, we propose a novel priority-setting process that integrates these potentially divergent perspectives using Reinforcement Learning from Human Feedback (RLHF). Our framework uses an iterative, interactive AI-driven approach to optimize conservation policies by combining initial data-driven proposals with local knowledge and values provided as human feedback. This feedback is converted into a dynamic reward structure, allowing the model to learn and incorporate granular preferences and constraints. Before real deployment, we propose an intermediate calibration step where Large Language Models simulate structured stakeholder feedback to optimize the integration pipeline. Our RLHF approach provides a flexible and powerful roadmap for allocating conservation resources holistically, effectively, and inclusively, thereby increasing the probability of achieving long-lasting biodiversity and societal improvements.
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The success of global conservation goals risks being undermined by conflicts that arise when high-level, data-driven priorities clash with local needs and contexts. While top-down systematic planning efficiently identifies priority areas using large-scale, multi-dimensional data, it neglects the input of local communities and stakeholders. Here, we propose a novel priority-setting process that integrates these potentially divergent perspectives using Reinforcement Learning from Human Feedback (RLHF). Our framework uses an iterative, interactive AI-driven approach to optimize conservation policies by combining initial data-driven proposals with local knowledge and values provided as human feedback. This feedback is converted into a dynamic reward structure, allowing the model to learn and incorporate granular preferences and constraints. Before real deployment, we propose an intermediate calibration step where Large Language Models simulate structured stakeholder feedback to optimize the integration pipeline. Our RLHF approach provides a flexible and powerful roadmap for allocating conservation resources holistically, effectively, and inclusively, thereby increasing the probability of achieving long-lasting biodiversity and societal improvements.
https://doi.org/10.32942/X2T651
Community-based Research, Environmental Policy, Life Sciences, Policy Design, Analysis, and Evaluation
Published: 2025-11-21 14:55
Last Updated: 2025-11-21 14:55
CC-By Attribution-NonCommercial-NoDerivatives 4.0 International
Data and Code Availability Statement:
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Language:
English
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- last seen: 2026-05-20T01:45:00.602351+00:00