EAGER: XAISE: Explainable Artificial Intelligence for Science and Engineering

EAGER：XAISE：科学与工程领域的可解释人工智能

• 批准号: 2331329
• 负责人: Alok Choudhary
• 金额: $ 30万
• 依托单位: Northwestern University
• 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: 2023-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331329&HistoricalAwards=false
• 关键词: EAGER; XAISE; Explainable; Artificial; Intelligence

The increasing availability of data from the first three paradigms of science (experiments, theory, and simulations), along with advances in artificial intelligence and machine learning (AI/ML) has offered unprecedented opportunities for accelerating scientific discoveries using data-driven science. In particular Deep Learning (DL) has emerged as a transformative technology for deriving insights from massive datasets in many scientific domains such as material science, life-science, drug design etc. However, interpretability and explainability of DL models remains a major issue and an open problem. The need for explainable AI is often crucial in science and engineering, with applications of national importance such as materials design, construction, transportation, health-sciences, energy storage, etc., where the cost of wrong decisions can be catastrophically large, making it critical to ensure that the model is not just quantitatively accurate but is in fact learning from the correct features, and learning things that make sense in an understandable manner. But an abstract view of explanability is extremely difficult, because explanation also requires context within the application domain. This project seeks to develop addresses explanability within the use of DL by incorporating and utilizing context from scientific application domains and by exploring traditional machine learning techniques. This project seeks to explore and investigate an approach of ML-DL integration to realize explainable AI in terms of the four NIST (National Institute of Standards and Technology) principles. The specific goals of this project are: to design, develop, and implement XAISE – a framework to enhance the explainability of AI models for science and engineering applications with minimal impact on accuracy; to adapt XAISE for heterogenous data types, e.g., numerical, images, etc.; to scale XAISE to be able to handle large, multi-dimensional data; and evaluate the applicability of XAISE for at least two application domains, including materials science and nanotechnology.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.

从第一的数据范式（实验，理论和仿真）中的数据越来越多，智能和机器学习（AI/ML）为使用数据驱动的Iens提供了加速科学发现的机会（DL）。 ）已成为一种变革性的一种变革性，用于从许多科学领域，生命科学，药物设计等中的大量数据集中获得见解。但是，DL模型的可解释性和可解释性仍然是一个主要问题，并且开放的需求是可解释的AI是OS的OS。科学和工程，具有国家重要性的应用，例如材料，侵害，运输，健康科学，能源储存等，整个错误决策的成本可能是灾难性的，使该模型对模型至关重要，不仅是定量的，而且是实际上，从正确的特征中，帽子以可觉得的方式有意义。 AI在四个NIST（国家标准和技术研究所）原则方面。为了适应异源数据类型，例如，Xaise的适用性，包括材料科学和纳米技术优点和更广泛的影响审查标准。