Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees

• URL: http://arxiv.org/abs/2301.11911v2
• Date: Sun, 18 Jun 2023 17:03:27 GMT
• Title: Multi-dimensional concept discovery (MCD): A unifying framework with completeness guarantees
• Authors: Johanna Vielhaben, Stefan Bl\"ucher, Nils Strodthoff
• Abstract summary: We propose Multi-dimensional Concept Discovery (MCD) as an extension of previous approaches that fulfills a completeness relation on the level of concepts. We empirically demonstrate the superiority of MCD against more constrained concept definitions.
• Score: 1.9465727478912072
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The completeness axiom renders the explanation of a post-hoc XAI method only locally faithful to the model, i.e. for a single decision. For the trustworthy application of XAI, in particular for high-stake decisions, a more global model understanding is required. Recently, concept-based methods have been proposed, which are however not guaranteed to be bound to the actual model reasoning. To circumvent this problem, we propose Multi-dimensional Concept Discovery (MCD) as an extension of previous approaches that fulfills a completeness relation on the level of concepts. Our method starts from general linear subspaces as concepts and does neither require reinforcing concept interpretability nor re-training of model parts. We propose sparse subspace clustering to discover improved concepts and fully leverage the potential of multi-dimensional subspaces. MCD offers two complementary analysis tools for concepts in input space: (1) concept activation maps, that show where a concept is expressed within a sample, allowing for concept characterization through prototypical samples, and (2) concept relevance heatmaps, that decompose the model decision into concept contributions. Both tools together enable a detailed understanding of the model reasoning, which is guaranteed to relate to the model via a completeness relation. This paves the way towards more trustworthy concept-based XAI. We empirically demonstrate the superiority of MCD against more constrained concept definitions.

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