[2603.29654] Concept frustration: Aligning human concepts and machine representations

Computer Science > Machine Learning arXiv:2603.29654 (cs) [Submitted on 31 Mar 2026] Title:Concept frustration: Aligning human concepts and machine representations Authors:Enrico Parisini, Christopher J. Soelistyo, Ahab Isaac, Alessandro Barp, Christopher R.S. Banerji View a PDF of the paper titled Concept frustration: Aligning human concepts and machine representations, by Enrico Parisini and 4 other authors View PDF HTML (experimental) Abstract:Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. We develop task-aligned similarity measures that detect concept frustration between supervised concept-based models and unsupervised representations derived from foundation models, and show that the phenomenon is detectable in task-aligned geometry while conventional Euclidean comparisons fail. Under a linear-Gaussian generative model we derive a closed-form expression for Bayes-optimal concept-based classifier accuracy, decomposing pre...