EAGER: XAISE: Explainable Artificial Intelligence for Science and Engineering

Northwestern University, Evanston IL

Investigators

Abstract

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.