Concept-Based Explanations

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability
May 19, 2026Giacomo Astolfi, Matteo Bianchi, Riccardo Campi +2Image Generation Concept-Based Explanations
Politecnico di Milano, DEIB, Italy
OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
May 18, 2026Chiara Maria Russo, Simone Carnemolla, Simone Palazzo +3Explainable Artificial Intelligence Concept-Based Explanations
University of Catania Catania, Italy
$α$ -TCAV: A Unified Framework for Testing with Concept Activation Vectors
May 15, 2026Ekkehard Schnoor, Jawher Said, Malik Tiomoko +2Concept-Based Explanations Neural Network Interpretability
Department of Computer Science Aalto University Espoo, Finland · Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin, Germany · Huawei Noah’s Ark Lab Huawei Technologies Paris, France +1
E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability
May 11, 2026Hasib Aslam, Muhammad Ali Chattha, Muhammad Taha Mukhtar +3Concept-Based Explanations Neural Network Interpretability
1National University of Sciences and Technology (NUST) · 2German Research Centre for Artificial Intelligence (DFKI)
Concept-Based Abductive and Contrastive Explanations for Behaviors of Vision Models
May 7, 2026Ronaldo Canizales, Divya Gopinath, Corina Păsăreanu +1Abductive Reasoning Concept-Based Explanations
Colorado State University, Fort Collins CO, USA · KBR Inc., NASA Ames, Moffett Field CA, USA · Carnegie Mellon University, Pittsburgh PA, USA
From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models
May 1, 2026Yearim Kim, Sangyu Han, Nojun KwakSaliency Map Evaluation Mechanistic Interpretability
Seoul National University, Seoul 08826, South Korea
Concept Graph Convolutions: Message Passing in the Concept Space
Apr 22, 2026Lucie Charlotte Magister, Pietro LioGraph Neural Networks Message Passing Neural Networks
University of Cambridge
Subgraph Concept Networks: Concept Levels in Graph Classification
Apr 20, 2026Lucie Charlotte Magister, Alexander Norcliffe, Iulia Duta +1Graph Neural Networks Hierarchical Representation Learning
University of Cambridge Cambridge, UK
When, How Long and How Much? Interpretable Neural Networks for Time Series Regression by Learning to Mask and Aggregate
Dec 3, 2025Florent Forest, Amaury Wei, Olga FinkConcept-Based Explanations Neural Network Interpretability
1IMOS Laboratory, EPFL, Lausanne, CH-1015, Switzerland.
VisionLogic: Discovering and Grounding Decision-Relevant Visual Concepts
Mar 13, 2025Chuqin Geng, Yuhe Jiang, Li Zhang +5Explainable Artificial Intelligence Concept-Based Explanations
School of Computer Science, McGill University, Montreal, Canada · Department of Computer Science, University of Toronto, Toronto, Canada
Compositional Concept-Based Neuron-Level Interpretability for Deep Reinforcement Learning
Feb 2, 2025Zeyu Jiang, Hai Huang, Xingquan ZuoReinforcement Learning Concept-Based Explanations
School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

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A Framework for Evaluating Zero-Shot Image Generation in Concept-based Explainability

Giacomo Astolfi, Matteo Bianchi, Riccardo Campi, Antonio De Santis+1

Politecnico di Milano, DEIB, Italy

Image Generation Concept-Based Explanations Explainability Evaluation

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Concept-based Explainable Artificial Intelligence (XAI) interprets deep learning models using human-understandable visual features (e.g., textures or object parts) by linking internal representations to class predictions, thereby bridging the gap between low-level image data and high-level semantics. A major challenge, however, is the reliance on large sets of labeled images to represent each concept, which limits scalability. In this work, we investigate the use of zero-shot Text-to-Image (T2I) generative models as a source of synthetic concept datasets for concept-based XAI methods. Specifically, we generate concepts using predefined prompts and evaluate their faithfulness to real ones through four complementary analyses: (1) comparing synthetic vs. real concept images via concept representation similarity; (2) evaluating their intra-similarity by comparing pairs of subsets of the same concept with progressively increasing size; (3) evaluating their performance for downstream explanation tasks using relevant class images; (4) evaluating how removing a concept from tested class images affects explanations of generated concepts. While current T2I generative models promise a shortcut to concept-based XAI, our study highlights challenges and raises open questions about the use of synthetic data generated by zero-shot pipelines in model analyses. The resulting dataset is available at https://github.com/DataSciencePolimi/ZeroShot-T2I-Concepts.

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