Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
This is an incremental review that addresses adapting XAI methods for geoscientific applications, targeting researchers in AI and geosciences.
The paper reviews prototype-based explainable AI methods, organizing literature into themes like prototype development and visualization, and discusses their potential application to geoscientific data, highlighting differences from standard benchmarks.
Prototype-based methods are intrinsically interpretable XAI methods that produce predictions and explanations by comparing input data with a set of learned prototypical examples that are representative of the training data. In this work, we discuss a series of developments in the field of prototype-based XAI that show potential for scientific learning tasks, with a focus on the geosciences. We organize the prototype-based XAI literature into three themes: the development and visualization of prototypes, types of prototypes, and the use of prototypes in various learning tasks. We discuss how the authors use prototype-based methods, their novel contributions, and any limitations or challenges that may arise when adapting these methods for geoscientific learning tasks. We highlight differences between geoscientific data sets and the standard benchmarks used to develop XAI methods, and discuss how specific geoscientific applications may benefit from using or modifying existing prototype-based XAI techniques.