CRII: III: Explainable Artificial Intelligence for Biodiversity Science & Conservation

生物物种的灭绝正在迅速加速。即使有高分辨率的信息，在预测物种的灭绝或种群减少时也经常涉及重大的不确定性。生物物种分类的变化是影响生物多样性保护和政策决策的关键挑战。分类是用文字描述的，这些文字提供了一个机会，可以使用自然语言处理和机器学习(ML)来澄清物种关系，并通过处理物种分类中的可变性来提供对灭绝风险的新见解。开发一种准确且可扩展的机器学习和人工智能(ML/AI)以实现“分类智能”，有助于支持保护决策的稳健性。这一点很重要，因为分类分类可以将一组生物移入或移出法律保护的考虑范围。人工智能可以帮助这种分类和支持保护项目的协调。这个项目的目标是开发AI/ML技术，通过在不同的分类学角度下预测物种分布和风险的不同或有结果，提供对灭绝风险的新见解。至关重要的是，得出的见解要为人类所理解，才能安全地将这些结果转化为可操作的建议。生物多样性数据，包括分类学和地理空间数据，对人工智能构成了独特的挑战，因为它们是异质的，结构复杂，并且经常变化。该项目旨在通过一种新的方法来应对这些挑战，该方法结合了来自相关科学出版物文本数据的自然语言处理(NLP)和自动归纳和演绎推理，包括结合分类因素和相关领域结构的定性空间推理，以发现人类可理解的知识，用于保护生物学应用。通过这样做，这个项目还有可能使人工智能超越单一的应用领域。该奖项将开展的研究活动包括在领域专家的帮助下进行数据和知识管理，以及开发和评估上述人工智能技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Extinction of biological species is accelerating rapidly. Significant uncertainty is often involved in predicting the extinction or population decline of species, even with high-resolution information. Changes in the taxonomic classification of biological species is a key challenge that impacts both biodiversity conservation and policy decisions. The taxonomy is described in words and these words provide an opportunity to use natural language processing and machine learning (ML) to clarify species relationships and provide novel insights into extinction risk by addressing the variability in species taxonomy. Developing an accurate and scalable machine learning and artificial intelligence (ML/AI) for “taxonomic intelligence” can help support the robustness of conservation decision making. This is important because the taxonomic classification can move a group of organisms in or out of consideration for legal protection. AI can help in this classification and support coordination of conservation projects.The goal of this project is to develop AI/ML techniques to provide novel insights into extinction risk, by projecting different contingent outcomes for species distributions and risks under different taxonomic perspectives. It is critical that the derived insights be understandable to humans, to safely translate these outcomes into operational recommendations. Biodiversity data, which include taxonomical and geospatial data, pose unique challenges to AI in that they are heterogeneous, structurally complex, and frequently change. This project aims to address these challenges with a novel approach combining Natural Language Processing (NLP) from the textual data of relevant scientific publications, and automated inductive and deductive reasoning, including qualitative spatial reasoning incorporating the taxonomic factor and relevant domain structures, for discovery of human-understandable knowledge for conservation biology applications. In doing so, this project also has the potential to advance AI beyond a single application domain. The research activities to be undertaken in this award include data and knowledge curation with the help of domain experts, and the development and evaluation of the aforementioned AI techniques.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.