Proceedings

The full proceedings can be found here.

Semirings Meet Interpretability: A Quantitative Logic for Model Explanations

Marcelo Arenas, Alexander Pinto

Beyond Output Confidence: A Mechanistic Interpretability Analysis of Sycophantic Vulnerability in Large Language Models

Harleen Kaur Bagga, Justin Shenk

Sequential Decision-Making with Explanatory Actions

Hendrik Baier, Roya Daneshi, Sarath Sreedharan

Counterfactuals by Design: Residual Decomposition and Back-to-Normality Editing for Time Series

Szymon Bobek, Syed Muhammad Hamza Zaidi, Myra Spiliopoulou, Grzegorz J. Nalepa

Command-Space Counterfactual Explanations for Pareto-Conditioned Reinforcement Learning

Joanikij Chulev, Hendrik Baier

Acceptance and Perception of AI: The Roles of Model Confidence, Correctness, and Counterfactual Explanations Formats

Xinyue Dai, Georgios Fragkoulidis, Guro Nordbø Braut, Paul Liston

CRISP - a Methodological Framework for Complexity-Guided Visual Hidden-Layer xAI

Simon Geerkens, Christian Sieberichs, Alexander Braun, Viswanathan Ramesh, Thomas Waschulzik

Forecasting-Conditioned Reinforcement Learning: Anticipatable Policies via Multi-Step Self-Forecasting

Jeremias Gerner, Klaus Bogenberger, Stefanie Schmidtner

Explaining Risk in High Stakes Domains

Assaf Gozlan, Claudia V. Goldman

Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation

Md Mohasin Hossain, Anar Amirli, Robert Leist, Md Abdul Kadir, Daniel Sonntag

Assessing Automatic Concept Extraction for Reinforcement Learning Policies

Chengpeng Hu, Yingqian Zhang, Hendrik Baier

The Directed Prediction Change - Efficient and Trustworthy Fidelity Assessment for Local Feature Attribution Methods

Kevin Iselborn, David Dembinsky, Adriano Lucieri, Andreas Dengel

Does Target Class Matter? A Reproducibility Study of Plausible Counterfactual Explanations for Image Classification

Jasmin Kareem, Annelot W. Bosman, Meike Nauta, Martijn C. Willemsen, Jan N. van Rijn, Maarten de Rijke

Interpretable Decoding of Cognitive States from fMRI

Valeria Kirova, Ivan Butakov, Alexander Semenenko, Alexey Frolov

Do Neural Networks Attend to Expert-Recognized Morphological Characters? XAI Case Study in Small-Data Zooplankton Classification

Valeria Kirova, Vitaliia Rubleva, Vladimir Valeyev, Anna Kasatkina, Valentin Toptunov, Egor Belianin, Petr Garibian

Counterfactual Explanations Under Concept Drift

Marcin Kostrzewa, Jerzy Stefanowski, Maciej Zięba

Hu-CEMNIST: A Benchmark Dataset of Human-Generated Counterfactual Explanations for MNIST

Ulrike Kuhl, André Artelt

Evaluating Explanation-Driven Vision–Language Reasoning via Generation Order Interventions

Siting Liang, Luca Rippe, Omar Adjali, Daniel Sonntag

CSTA++: Unifying State, Action Temporal Abstractions and Causal Information for Contrastive Policy Explanations

Xiaowei Liu, Yining Yuan, Weiru Liu

Toward Template-Free Explainability for Monte Carlo Tree Search

Siqi Lu, Mirsaleh Bahavarnia, Hiba Baroud, Yixuan Zhang, Hemant Purohit, Ayan Mukhopadhyay

BXRL: Behavior-Explainable Reinforcement Learning

Ram Rachum, Yotam Amitai, Yonatan Nakar, Reuth Mirsky, Cameron Allen

Evaluating RL Explainability Methods by How Much They Help Fix Bugs in Agents

Ram Rachum, Yotam Amitai, Bálint Gyevnár, Reuth Mirsky, Cameron Allen

Radiologist-Guided Causal Concept Bottleneck Models for Chest X-Ray Interpretation

Amy Rafferty, Rishi Ramaesh, Ajitha Rajan

Out-of-Distribution Detection Using Counterfactual Distance

Maria Stoica, Francesco Leofante, Alessio Lomuscio

Improving Human Oversight of AI Systems With Expert Feedback Using Interactive, Contrastive Explanations

Zhengyu Su, Mark Keane, Eoin Delaney

When Superpixels Fail on Documents: A Study of Segmentation for LIME Explanations

Quentin Telnoff, Emanuela Boros, Mickaël Coustaty, Robin Jarry, Fabrice Crohas, Antoine Doucet

Global Explanations for Multivariate Time Series Forecasting Models via K-Order Markov Approximations

Amadeo Tunyi

The Failures of Marginal Influence-Based Attribution Methods for Global Time Series Explanations

Amadeo Tunyi

Part-based Quantitative Analysis for Heatmaps

Osman Tursun, Sinan Kalkan, Simon Denman, Sridha Sridharan, Clinton Fookes

Addressing the Selection Problem in Explainable AI

Claire Vlases, Katelyn Morrison

On the Reliability of Post-Hoc Attributions Under Adversarial Training

Maximilian Wendlinger, Paul-Andrei Sava, Wei Herng Choong, Ching-Yu Kao, Daniel Kowatsch

Towards an Argumentative Foundation for Evaluative AI

Xiang Yin, Tim Miller, Nico Potyka, Antonio Rago, Francesca Toni

FlagGAM: Rule-Based Generalized Additive Modeling for Explainable Tabular Prediction

Zijie Zhao, Roy E. Welsch

Faithfulness Without Anchors: A Position Paper on the Structural Misalignments of Explainable AI Faithfulness Metrics for Feature Importance

Marco Zullich, Emily Schiller, Ivan Gentile, David Dembinsky, Adriano Lucieri, Kilian Göller, Steffen Seitz

Accepted but could not be presented:

Two Black Boxes, One Solver: Encoder Probing and Decoder Attribution for Neural Multi-Attribute VRP under Hard-Mask and Recourse Decoders

Sohaib Afifi

Why Textual Rule Explanations Change Across Runs in Reinforcement Learning

Yulong Wu