Explainability Evaluation

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27 papers in the last four weeks, up 50% on the four weeks before. 0.3% of all new papers.

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Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment
May 15, 2026Till Beemelmanns, Shayan Sharifi, Manas Mehrotra +2Explainable Artificial Intelligence Autonomous Driving Perception
Institute for Automotive Engineering, RWTH Aachen University, 52074 Aachen, Germany
AGOP-IxG: A Gradient Covariance Filter for Local Feature Attribution on Tabular Data, with a Controlled Benchmark
May 15, 2026Raj Kiran Gupta KatakamGradient-Based Attribution Feature Attribution
Credit Karma
$α$ -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
GESD: Beyond Outcome-Oriented Fairness
May 14, 2026Gideon Popoola, John SheppardAlgorithmic Fairness Group Fairness
Montana State University Bozeman, MT, USA
AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph Kernel Networks
May 14, 2026Magdalena Proszewska, N. SiddharthGraph Neural Networks Interpretable ML
School of Informatics, University of Edinburgh
How to Evaluate and Refine your CAM
May 14, 2026Luca Domeniconi, Alessandra Stramiglio, Michele Lombardi +1Feature Attribution Explainability Evaluation
University of Bologna, Bologna, Italy
Architecture-Aware Explanation Auditing for Industrial Visual Inspection
May 14, 2026Sibo Jia, Zihang Zhao, Kunrong LiIndustrial Inspection Explainability Evaluation
Dublin College, Beijing University of Technology, Beijing, P.R. China · College of Computer Science, Beijing University of Technology, P.R. China
Descriptive Collision in Sparse Autoencoder Auto-Interpretability: When One Explanation Describes Many Features
May 13, 2026Jordan F. McCannLLM Interpretability Sparse Autoencoders
Independent Researcher
Mechanism Plausibility in Generative Agent-Based Modeling
May 12, 2026Patrick Zhao, David Huu Pham, Nicholas VincentLLM Agent Evaluation Agent-Based Modeling
Simon Fraser University, Burnaby, BC, Canada
Persistent and Conversational Multi-Method Explainability for Trustworthy Financial AI
May 12, 2026Georgios Makridis, Georgios Fatouros, John Soldatos +2Explainable Artificial Intelligence Explainability Evaluation
University of Piraeus, Greece · ExpertAI-Lux S.à r.l · Department of Digital Systems
Interpretability Can Be Actionable
May 11, 2026Hadas Orgad, Fazl Barez, Tal Haklay +9Explainable Artificial Intelligence Neural Network Interpretability
Kempner Institute at Harvard University · University of Oxford · Martian +10
Interpretable Machine Learning for Football Performance Analysis: Evidence of Limited Transferability from Elite Leagues to University Competition
May 11, 2026Yu-Fang Tsai, Yu-Jen Chen, Kok-Hua Tan +5Distribution Shift Robustness SHAP Feature Attribution
Department of Kinesiology, National Tsing Hua University, Hsinchu, Taiwan · Department of Computer Science, National Tsing Hua University, Hsinchu, Taiwan · Physical Education Office, National Tsing Hua University, Hsinchu, Taiwan +4
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)
Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality
May 11, 2026Mateusz Cedro, Marcin ChlebusNeural Network Interpretability Explainability Evaluation
University of Antwerp, Antwerp, 2000, Belgium · University of Warsaw, Warsaw, 00-241, Poland
Useful for Exploration, Risky for Precision: Evaluating AI Tools in Academic Research
May 11, 2026Anthea Dathe, Kiran Hoffmann, Aline MangoldAI-Assisted Scientific Research Scientific Reproducibility
Department Speculative Transformation Dresden University of Technology Dresden, 01062
Explanation-Aware Learning for Enhanced Interpretability in Biomedical Imaging
May 11, 2026Zubair Faruqui, Rahul DubeyMedical Image Classification Explainable Medical Image Analysis
Department of Computer Science, Missouri State University
Fairness of Explanations in Artificial Intelligence (AI): A Unifying Framework, Axioms, and Future Direction toward Responsible AI
May 11, 2026Gideon Popoola, John SheppardExplainable Artificial Intelligence Algorithmic Auditing
Montana State University, Bozeman, Montana, USA
From Mechanistic to Compositional Interpretability
May 9, 2026Ward Gauderis, Thomas Dooms, Steven T. Homer +2Mechanistic Interpretability Neural Network Interpretability
Vrije Universiteit Brussel, Brussels, Belgium · Independent · UK AI Security Institute, London, United Kingdom.
Effective Explanations Support Planning Under Uncertainty
May 8, 2026Hanqi Zhou, Britt Besch, Charley M. Wu +1LLM Planning Explainability Evaluation
University of Tübingen · Technical University Darmstadt · Max Planck Institute for Biological Cybernetics +2
Position: Mechanistic Interpretability Must Disclose Identification Assumptions for Causal Claims
May 8, 2026Zezheng Lin, Fengming LiuMechanistic Interpretability Causal Identifiability
Preprint.
AI-Generated Images: What Humans and Machines See When They Look at the Same Image
May 7, 2026Silvia Poletti, Justin Ilyes, Marcel Hasenbalg +2Explainable Artificial Intelligence AI-Generated Image Detection
Austrian Institute of Technology, Vienna, Austria
Evaluating Explainability in Safety-Critical ATR Systems: Limitations of Post-Hoc Methods and Paths Toward Robust XAI
May 7, 2026Vanessa Buhrmester, David Muench, Dimitri Bulatov +1Explainable Artificial Intelligence Explanation Stability
IOSB, Ettlingen, Germany
Cross-Model Consistency of Feature Importance in Electrospinning: Separating Robust from Model-Dependent Features
May 6, 2026Mehrab Mahdian, Ferenc Ender, Tamas PardyMaterials Science Explanation Stability
Thomas Johann Seebeck Department of Electronics, Tallinn University of Technology, Tallinn, Estonia · Department of Electron Devices, Budapest University of Technology and Economics, Budapest, Hungary
Evaluation Cards for XAI Metrics
May 6, 2026Rokas Gipiškis, Olga KurasovaAI Accountability Explainability Evaluation
1Vilnius University · 2AI Standards Lab
Atomic Fact-Checking Increases Clinician Trust in Large Language Model Recommendations for Oncology Decision Support: A Randomized Controlled Trial
May 5, 2026Lisa C. Adams, Linus Marx, Erik Thiele Orberg +8Healthcare Clinical Decision Support
Department of Diagnostic and Interventional Radiology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Radiation Oncology, TUM University Hospital Rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany · Department of Internal Medicine III: Hematology and Clinical Oncology, University Hospital Regensburg, University of Regensburg, Regensburg, Germany +4
GRAFT: Auditing Graph Neural Networks via Global Feature Attribution
May 5, 2026Rishi Raj Sahoo, Subhankar MishraFeature Attribution Graph Neural Networks
National Institute of Science Education and Research (NISER), Bhubaneswar, India · Homi Bhabha National Institute, Mumbai, India
Bucketing the Good Apples: A Method for Diagnosing and Improving Causal Abstraction
May 4, 2026Li Puyin, Jiyuan Tan, Ahmad Jabbar +2Mechanistic Interpretability Causal Abstraction
♢Stanford University · ♠Goodfire
How Can One Choose the Best CAM-Based Explainability Method for a CNN Model?
May 3, 2026Daniel da Silva Costa, Pedro Nuno de Souza Moura, Adriana C. F. AlvimFeature Attribution Saliency Map Evaluation
Postgraduate Program in Informatics, Federal University of the State of Rio de Janeiro (UNIRIO), Rio de Janeiro, RJ, 22290-240, Brazil
LLMs Should Not Yet Be Credited with Decision Explanation
May 1, 2026Wenshuo WangCognitive Modeling LLM Decision-Making
School of Future Technology, South China University of Technology, China
An End-to-End Decision-Aware Multi-Scale Attention-Based Model for Explainable Autonomous Driving
Apr 30, 2026Maryam Sadat Hosseini Azad, Shahriar Baradaran Shokouhi, Amir Abbas Hamidi Imani +2Explainable Artificial Intelligence Autonomous Driving
Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran · Department of Computing Science, University of Alberta, Edmonton, AB T6G, Canada

May 15, 2026, cs.ROJ/K move · Enter open · S save

Towards Trustworthy and Explainable AI for Perception Models: From Concept to Prototype Vehicle Deployment

Till Beemelmanns, Shayan Sharifi, Manas Mehrotra, Ayushman Choudhuri+1

Institute for Automotive Engineering, RWTH Aachen University, 52074 Aachen, Germany

Explainable Artificial Intelligence Autonomous Driving Perception Explainability Evaluation Uncertainty Calibration

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Deep Neural Networks have become the dominant solution for Autonomous Driving perception, but their opacity conflicts with emerging Trustworthy AI guidelines and complicates safety assurance, debugging, and human oversight. While theoretical frameworks for safe and Explainable AI (XAI) exist, concrete implementations of Trustworthy AI for 3D scene understanding remain scarce. We address this gap by proposing a Trustworthy AI perception module that is remarkably robust, integrates faithful explainability, and calibrated uncertainty estimates. Building on a transformer-based detector, we derive explanation from the attention mechanism at inference time and validate their faithfulness using perturbation-based consistency tests. We further integrate an uncertainty estimation and calibration module, and apply robustness-enhancing training methods. Experiments show faithful saliency behavior, improved robustness, and well-calibrated uncertainty estimates. Finally, we deploy these Trustworthy AI elements in a prototype vehicle and provide an XAI Interface that visualizes documentation artifacts, model uncertainty state, and saliency maps, demonstrating the feasibility of trustworthy perception monitoring in real time. Supplementary materials are available at https://tillbeemelmanns.github.io/trustworthy_ai/ .

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