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: 2246032
• 负责人: Atriya Sen
• 金额: $ 17.48万
• 依托单位: University of New Orleans
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2024-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2246032&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，即使有高分辨率信息，预测物种灭绝或种群数量下降也往往存在很大的不确定性。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一个关键挑战。分类法是用文字描述的，这些文字提供了使用自然语言处理和机器学习 (ML) 来澄清物种关系的机会，并通过开发准确且可扩展的物种分类法来提供对灭绝风险的新颖见解。用于“分类智能”的机器学习和人工智能（ML/AI）可以帮助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体移入或移出法律保护的考虑范围。该项目的目标是开发人工智能/机器学习技术，通过预测不同分类学角度下物种分布和风险的不同偶然结果，为灭绝风险提供新的见解。得出的见解能够为人类所理解，将这些结果安全地转化为操作建议，包括分类和地理空间数据，对人工智能提出了独特的挑战，因为它们是异构的、结构复杂的并且经常变化，该项目旨在通过结合自然语言的新颖方法来解决这些挑战。对相关科学出版物的文本数据进行处理（NLP），以及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以用于保护生物学应用。因此，该项目还有潜力推动人工智能超越单一应用领域。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，以及上述人工智能技术的开发和评估。授予 NSF 的法定使命，并通过评估反映使用基金会的智力优点和更广泛的影响审查标准，被认为值得支持。