Prototypical Self-Explainable Models Without Re-training
Srishti Gautam Ahcene Boubekki Marina M. C. Höhne Michael C. Kampffmeyer
Explainable AI (XAI) has unfolded in two distinct research directions with,
on the one hand, post-hoc methods that explain the predictions of a pre-trained
black-box model and, on the other hand, self-explainable models (SEMs) which
are trained directly to provide explanations alongside their predictions. While
the latter is preferred in most safety-critical scenarios, post-hoc approaches
have received the majority of attention until now, owing to their simplicity
and ability to explain base models without retraining. Current SEMs instead,
require complex architectures and heavily regularized loss functions, thus
necessitating specific and costly training. To address this shortcoming and
facilitate wider use of SEMs, we propose a simple yet efficient universal
method called KMEx (K-Means Explainer), which can convert any existing
pre-trained model into a prototypical SEM. The motivation behind KMEx is to
push towards more transparent deep learning-based decision-making via
class-prototype-based explanations that are guaranteed to be diverse and
trustworthy without retraining the base model. We compare models obtained from
KMEx to state-of-the-art SEMs using an extensive qualitative evaluation to
highlight the strengths and weaknesses of each model, further paving the way
toward a more reliable and objective evaluation of SEMs.