Explanation Evaluation

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Overview

Explanation evaluation studies whether an explanation accurately reflects a model’s decisions and helps people understand or act on them. Approaches include perturbation and intervention tests, sufficiency and necessity measures, localization against ground truth, and checks for stability, simplicity, or alignment with domain knowledge. Human studies and LLM-based judges assess qualities such as clarity, trust, and educational value, while task-based tests examine whether explanations actually improve decisions or learning. A central tension is that scores depend on the intervention, reference, and audience, and faithful or polished explanations do not necessarily help users or support correct decisions.

Latest papers

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26 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

  1. Evidence-Bound Reasoning: Neuro-Semantic Verification of Biomedical AI in Glioblastoma Radiogenomics

    Oct 6, 2026Mariya Miteva, Maria Nisheva-PavlovaExplainable Medical Image AnalysisRadiomics

    Faculty of Mathematics and Informatics – Sofia University St. Kliment Ohridski, 5 James Bourchier Blvd., Sofia 1164, Bulgaria

  2. CHARTER: Auditing Reference Substitution in Hierarchical Compact-Evidence Evaluation for Computational Pathology

    Oct 6, 2026Hyun Do Jung, Jungwon Choi, Soojung Choi +2Computational PathologyAlgorithmic Auditing

    Yonsei University · KAIST · Hallym University Chuncheon Sacred Heart Hospital

  3. A Synthetic Ground-Truth Framework for the Evaluation of Explainable AI Methods

    Sep 24, 2026Miquel Miró-Nicolau, Francesco Spinnato, Riccardo GuidottiExplainability EvaluationExplanation Evaluation

    Universitat de les Illes Balears, Cra. de Valldemossa, km 7.5., Palma, 07122, Spain · Uiniversity of Pisa, Largo B. Pontecrovo, 3, 56127, Pisa, Italy · ISTI-CNR, Via G. Moruzzi, 1, Pisa, 56127, Italy

  4. Evaluating Explanation Methods by the Predictors They Induce

    Sep 17, 2026Jacob Selbæk, Hugo L. HammerFeature AttributionSHAP Feature Attribution

    Department of Computer Science, Oslo Metropolitan University, Oslo, Norway · Department of Holistic Systems, SimulaMet, Oslo, Norway · Department of Plastic and Reconstructive Surgery, Oslo University Hospital, Oslo, Norway

  5. XAI-Arena: Can LLMs Assess the Quality of XAI Explanations?

    Sep 8, 2026Yanfei Hu Fleischhauer, Alona Zharova, Nadja Klein +1Explainable Artificial IntelligenceLLM-as-a-Judge

    LMU Munich, Germany · Chair of Information Systems, School of Business and Economics, Humboldt-Universität zu Berlin, Germany · Scientific Computing Center, Karlsruhe Institute of Technology, Germany +1

  6. Self-Explanation Tutor for Active Study of CS1 Worked Examples

    Aug 25, 2026Arun-Balajiee Lekshmi-Narayanan, Mohammad Hassany, Kamil Akhuseyinoglu +2Intelligent Tutoring SystemsExplanation Evaluation

    University of Pittsburgh Pittsburgh, PA, USA · Carnegie Mellon University Pittsburgh, PA, USA

  7. Prediction certification cannot replace explanation certification: a competence envelope for trustworthy AI under compound stress

    Aug 21, 2026Nataliya Shakhovska, Ivan Izonin, Stergios-Aristoteles MitoulisTrust in AIExplainability Evaluation

    Lviv Polytechnic National University, Lviv, Ukraine. · Centre for Global Infrastructure Resilience, The Bartlett School of Sustainable Construction, University College London, UK

  8. A Cognitively Motivated Multidimensional Framework for Evaluating Metaphor Explanations

    Aug 16, 2026Ana Naveriani, Jakob Suchan, Stefano Zoia +3Explanation EvaluationFigurative Language Understanding

    Constructor University · University of Turin · Örebro University +2

  9. AIriskEval-edu Demo: Auditing of Pedagogical Risks in Educational Explanations

    Jul 28, 2026Javier Irigoyen, Roberto Daza, Francisco Jurado +5Generative AI in EducationExplainability Evaluation

    BiometricsAI, Universidad Autónoma de Madrid (UAM), Spain · GHIA, Universidad Autónoma de Madrid (UAM), Spain · Universidad de Las Palmas de Gran Canaria (ULPGC), Spain

  10. Towards a Unified Multidimensional Explainability Metric: Evaluating Trustworthiness in AI Models

    Jul 15, 2026Georgios Makridis, Georgios Fatouros, Athanasios Kiourtis +4Feature AttributionExplainability Evaluation

    Department of Digital Systems University of Piraeus Piraeus, Greece · Department of Business Administration University of Piraeus Piraeus, Greece · Innov-Acts Ltd Innov-Acts Nicosia, Cyprus

  11. Measuring Judgment Quality in Natural-Language Explanations: Evidence from Forecasting Tournaments

    Jun 29, 2026Christopher W. Karvetski, Sheldon S. Huang, Simas Kučinskas +4LLM-as-a-JudgeProbabilistic Forecasting

    Forecasting Research Institute · Good Judgment Inc · University of Toronto +4

  12. What LLMs explain is not what they believe: Evaluating explanation sufficiency under models' own input beliefs

    Jun 26, 2026Nhi Nguyen, Shauli Ravfogel, Rajesh RanganathLLM EvaluationLLM Interpretability

    New York University

  13. A Good Talk Does not Look Like a Summary, It Teaches You! Measuring Takeaways from Paper-to-Video Talks

    Jun 26, 2026Ishani Mondal, Aparna Garimella, Ananya Sai +2T2V GenerationScientific Communication

    University of Maryland, College Park · Adobe Research

  14. Few-class Fidelity: Evaluating Explanations of Real-conditions CNN classifiers with Optimized Perturbations

    Jun 23, 2026Wistan Marchadour, Pedro Soto Vega, Franck Vermet +1Explainable Medical Image AnalysisExplainability Evaluation

    LaTIM, INSERM, UMR 1101, Univ Brest, Brest, France · LMBA, CNRS, UMR 6205, Univ Brest, Brest, France

  15. A Definition of Good Explanations and the Challenges Explaining LLM Outputs

    Jun 12, 2026Louis Mahon, Elliot Ford, Callum HackettExplainable Artificial IntelligenceCounterfactual Explanations

    UnlikelyAI, London, UK

  16. Disagreeing Rationales: Rethinking Classification and Explainability Evaluation in Hate Speech Detection

    May 29, 2026Benedetta Muscato, Beiduo Chen, Gizem Gezici +2Annotator DisagreementHate Speech Detection

    Scuola Normale Superiore, Italy · Munich Center for Machine Learning, Germany · UMaiNLP, LMU Munich, Germany

  17. Quality Without Usefulness: LLM-Generated XAI Narratives as Trust Heuristics Rather Than Decision Aids

    May 26, 2026Fabian Lukassen, Jan Herrmann, Christoph Weisser +3LLM EvaluationExplainable Artificial Intelligence

    University of Göttingen · BASF SE · Hochschule Bielefeld +1

  18. Learning Quantifiable Visual Explanations Without Ground-Truth

    May 18, 2026Amritpal Singh, Andrey Barsky, Mohamed Ali Souibgui +2Perturbation-Based Feature AttributionCausal Interpretability

    Computer Vision Center, Barcelona, Spain · Autonomous University of Barcelona, Spain

  19. Scaling Vision Models Does Not Consistently Improve Localisation-Based Explanation Quality

    May 11, 2026Mateusz Cedro, Marcin ChlebusNeural Network InterpretabilityExplainability Evaluation

    University of Antwerp, Antwerp, 2000, Belgium · University of Warsaw, Warsaw, 00-241, Poland

  20. Evaluating the Alignment Between GeoAI Explanations and Domain Knowledge in Satellite-Based Flood Mapping

    Apr 28, 2026Hyunho Lee, Wenwen LiRemote SensingExplainability Evaluation

    School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, 85281, AZ, USA

  21. A Two-Stage LLM Framework for Accessible and Verified XAI Explanations

    Apr 14, 2026Georgios Mermigkis, Dimitris Metaxakis, Marios Tyrovolas +4Explainable Artificial IntelligenceFaithfulness of Language Model Explanations

    Department of Computer Engineering and Informatics, University of Patras, Patras, Greece · Archimedes Unit, Athena Research Center, Athens, Greece · Department of Informatics and Telecommunications, University of Ioannina, Arta, Greece +1

  22. TSQueryBench: LLM-as-a-Judge for Time Series Explanations

    Apr 2, 2026Preetham Sivalingam, Murari Mandal, Dhruv Kumar +1Explanation EvaluationExplainability Evaluation

    BITS Pilani, Pilani, India · KIIT & Birla AI Labs, Bhubaneshwar, India · BITS Pilani & Birla AI Labs, Pilani, India +1

  23. Structural Compactness as a Complementary Criterion for Explanation Quality

    Mar 31, 2026Mohammad Mahdi Mesgari, Jackie Ma, Wojciech Samek +2Explainability EvaluationExplanation Evaluation

    Fraunhofer Heinrich Hertz Institute, Berlin, Germany · Technische Universität Berlin, Berlin, Germany · BIFOLD – Berlin Institute for the Foundations of Learning and Data, Berlin, Germany +1

  24. Visual or Textual: Effects of Explanation Format and Personal Characteristics on the Perception of Explanations in an Educational Recommender System

    Mar 26, 2026Qurat Ul Ain, Mohamed Amine Chatti, Nasim Yazdian Varjani +2Explainable RecommendationExplanation Evaluation

    University of Duisburg-Essen Duisburg, Germany · RWTH Aachen University Aachen, Germany

  25. ICE: Intervention-Consistent Explanation Evaluation with Statistical Grounding for LLMs

    Mar 19, 2026Abhinaba Basu, Pavan ChakrabortyExplanation EvaluationLLM Interpretability

    1Indian Institute of Information Technology, Allahabad (IIITA) · 2National Institute of Electronics and Information Technology (NIELIT)

  26. EduEVAL-DB: A Role-Based Dataset for Pedagogical Risk Evaluation in Educational Explanations

    Feb 17, 2026Javier Irigoyen, Roberto Daza, Aythami Morales +4Explanation EvaluationGenerative AI in Education

    BiometricsAI, Universidad Autónoma de Madrid, Campus de Cantoblanco, Madrid, 28049, Spain · Group for Advanced Interactive Tools (GHIA), Universidad Autónoma de Madrid, Campus de Cantoblanco, Madrid, 28049, Spain · Department of Mathematics, Universidad de Las Palmas de Gran Canaria, 35017, Spain