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

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

27 Jan 2023arXiv:2301.11911archive 2025-07-28

Johanna Vielhaben, Stefan Blücher, Nils Strodthoff

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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jvielhaben/mcd-xai officialmentioned in papermentioned on GitHubpytorch report
grobruegge/hu-mcd mentioned on GitHubpytorch report

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check_folder grobruegge/hu-mcd/run_humcd.py community (archive-listed) ran · our draft was wrong MIT (permissive) · d47cbf11f1e737d3 · report
get_top_concept_segms grobruegge/hu-mcd/run_humcd.py community (archive-listed) ran · fixture could not drive it fingerprinted MIT (permissive) · e6c80da30245855f · report
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