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

CRII：III：生物多样性科学的可解释人工智能

• 批准号: 2426835
• 负责人: Atriya Sen
• 金额: $ 17.48万
• 依托单位: Oklahoma State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-03-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2426835&HistoricalAwards=false
• 关键词: CRII; III; Explainable; Artificial; Intelligence

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.

生物物种的灭绝正在迅速加速。 IES的关系因物种分类法的变化而灭绝。或出于法律保护的考虑。可以理解的是，将结果转化为空间数据。包括定性的空间推理，结合了分类学因素和相关领域结构，在这样做的情况下，人类融合了保护生物学应用程序，可以借助域名的数据和知识范围。以及对上述AI技术的发展和评估。该奖项反映了NSF的Beend Demed值得支持Thalugh评估，并使用基金会的UAL功绩和更广泛的影响审查标准。