Towards a fuller understanding of neurons with Clustered Compositional Explanations
This work addresses the issue of incomplete neuron explanations in interpretable AI, which is incremental as it builds on existing Compositional Explanations methods.
The paper tackles the problem of incomplete neuron behavior explanations by proposing Clustered Compositional Explanations, which combine clustering and a novel search heuristic to approximate a broader spectrum of activations, resulting in more comprehensive insights.
Compositional Explanations is a method for identifying logical formulas of concepts that approximate the neurons' behavior. However, these explanations are linked to the small spectrum of neuron activations (i.e., the highest ones) used to check the alignment, thus lacking completeness. In this paper, we propose a generalization, called Clustered Compositional Explanations, that combines Compositional Explanations with clustering and a novel search heuristic to approximate a broader spectrum of the neurons' behavior. We define and address the problems connected to the application of these methods to multiple ranges of activations, analyze the insights retrievable by using our algorithm, and propose desiderata qualities that can be used to study the explanations returned by different algorithms.