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.

生物物种的灭绝迅速加速。即使有高分辨率信息，预测物种的延伸或种群下降也经常涉及明显的不确定性。生物种类分类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。分类学用文字描述，这些单词提供了使用自然语言处理和机器学习（ML）来澄清开发准确且可扩展的机器学习和人工智能（ML/AI）进行“分类学智能”的机会，这可以帮助支持保护决策的鲁棒性。这很重要，因为分类学分类可以将一组生物移入或不考虑法律保护。人工智能可以帮助保护项目的分类并支持协调。该项目的目的是开发AI/ML技术，通过在不同的分类角度下针对物种分布和风险进行不同的偶然结果，以提供扩展风险的新见解。至关重要的是，对人类的衍生见解是可以理解的，将这些结果安全地转化为运营建议。包括分类学和地理空间数据在内的生物多样性数据对AI构成了独特的挑战，因为它们是异质的，结构上的复杂且经常变化的。该项目旨在通过一种新型方法来解决这些挑战，该方法将自然语言处理（NLP）结合在相关科学出版物的文本数据中，以及自动归纳和演绎推理，包括融合了分类学因素和相关领域结构的定性空间推理，以发现人为理解的人类知识知识对保护生物学应用的发现。这样一来，该项目也有可能将AI超越单个应用程序域。该奖项中要进行的研究活动包括在领域专家的帮助下进行数据和知识策划，以及优先AI技术的发展和评估。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的影响标准来评估来获得的支持。