ConceptDistil: Model-Agnostic Distillation of Concept Explanations
This work addresses the interpretability gap for non-technical users by enabling concept explanations for any black-box model, though it is incremental as it builds on existing distillation and concept-based methods.
The authors tackled the problem of providing concept-based explanations for any black-box classifier by proposing ConceptDistil, a method using knowledge distillation, and validated it in a real-world use-case to optimize both concept prediction and model mimicry.
Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg, neural networks) or data types (eg, images), and by either trying to extract concepts from an already trained network or training self-explainable models through multi-task learning. In this work, we propose ConceptDistil, a method to bring concept explanations to any black-box classifier using knowledge distillation. ConceptDistil is decomposed into two components:(1) a concept model that predicts which domain concepts are present in a given instance, and (2) a distillation model that tries to mimic the predictions of a black-box model using the concept model predictions. We validate ConceptDistil in a real world use-case, showing that it is able to optimize both tasks, bringing concept-explainability to any black-box model.