Learning by Self-Explaining
This work addresses the need for better learning mechanisms in AI, particularly for image classification, by introducing a novel self-explaining approach, though it appears incremental as it builds on existing concepts like self-refining AI and human-guided explanatory machine learning.
The paper tackles the problem of improving AI model learning by integrating self-explanations, inspired by human psychology, and shows that their Learning by Self-Explaining (LSX) workflow enhances model generalization, reduces confounding factors, and yields more task-relevant and faithful explanations.
Much of explainable AI research treats explanations as a means for model inspection. Yet, this neglects findings from human psychology that describe the benefit of self-explanations in an agent's learning process. Motivated by this, we introduce a novel workflow in the context of image classification, termed Learning by Self-Explaining (LSX). LSX utilizes aspects of self-refining AI and human-guided explanatory machine learning. The underlying idea is that a learner model, in addition to optimizing for the original predictive task, is further optimized based on explanatory feedback from an internal critic model. Intuitively, a learner's explanations are considered "useful" if the internal critic can perform the same task given these explanations. We provide an overview of important components of LSX and, based on this, perform extensive experimental evaluations via three different example instantiations. Our results indicate improvements via Learning by Self-Explaining on several levels: in terms of model generalization, reducing the influence of confounding factors, and providing more task-relevant and faithful model explanations. Overall, our work provides evidence for the potential of self-explaining within the learning phase of an AI model.