Prototype-Based Methods in Explainable AI and Emerging Opportunities in the Geosciences
Keywords: explainable AI, geosciences, case-based reasoning, prototype-based learning
TL;DR: A review of existing prototype-based inherently interpretable XAI literature and highlighting emerging opportunities in the geosciences.
Abstract: 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.
Submission Number: 145
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