A High-Resolution Landscape Dataset for Concept-Based XAI With Application to Species Distribution Models
arXiv:2604.13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management. Species distribution models (SDMs) are the primary tools for this task, serving two purposes: achieving robust predictive performance while providing ecological insights into the driving factors of distribution. However, the increasing complexity of deep learning SDMs has made extracting these insights more challenging. To reconcile these objectives, we propose the first implementation of concept-based Explainable AI (XAI) f
The increasing complexity of deep learning models in fields like species distribution analysis necessitates new methods to explain their decisions, pushing the development of concept-based Explainable AI.
This development addresses a critical challenge in AI adoption by enabling better understanding and trust in complex AI systems, particularly in sensitive environmental and scientific applications.
AI's predictive capabilities are now complemented by a more transparent and interpretable decision-making process, allowing human experts to gain ecological insights directly from model outputs.
- · Environmental scientists
- · Conservation organizations
- · AI explainability researchers
- · Deep learning application developers
- · Black box AI models in critical applications
- · Organizations relying solely on opaque AI without interpretability
More widespread and trusted deployment of AI in scientific and regulatory domains.
Accelerated discovery of underlying ecological principles through interpretable AI insights.
New policy frameworks and ethical guidelines for AI based on enhanced transparency and explainability.
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Read at arXiv cs.LG