Feature Attribution

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Overview

Feature attribution studies how to assign responsibility for a model’s prediction to parts of its input, such as image regions, text tokens, time-series measurements, or graph features. Common approaches include gradients and relevance propagation, perturbation and masking, Shapley values, and counterfactual or causal interventions; some methods instead build explanations into the model. Researchers evaluate explanations for faithfulness, stability, sparsity, localization, and usefulness across tasks such as image classification, language modeling, medical prediction, and time-series forecasting. A central challenge is that attributions can change with the model architecture, baseline, perturbation choices, or correlations among features, so no single method or metric is reliable in every setting.

Latest papers

CardsList

234 papers20 papers in the last four weeks, down 17% on the four weeks before. 0.1% of all new papers.

  1. Post-Hoc Uncertainty-Aware Explanations for Deployed Power Quality Disturbance Classifiers via Laplace Approximation

    Apr 15, 2026Yinsong Chen, Samson S. Yu, Kashem M. MuttaqiExplainable Artificial IntelligenceFeature Attribution

    School of Engineering, Deakin University, Melbourne, 3216, VIC, Australia · ARC Training Centre in Energy Technologies for Future Grids, School of Engineering, University of Wollongong, Wollongong, 2522, NSW, Australia

  2. Hessian-Enhanced Token Attribution (HETA): Interpreting Autoregressive LLMs

    Apr 14, 2026Vishal Pramanik, Maisha Maliha, Nathaniel D. Bastian +1Feature AttributionLLM Interpretability

    Department of Computer & Information Science & Engineering, University of Florida, Gainesville, FL 32611, USA · School of Computer Science, University of Oklahoma, Norman, OK 73019, USA · Department of Electrical Engineering and Computer Science, United States Military Academy, West Point, NY 10996, USA

  3. Trustworthy Feature Importance Avoids Unrestricted Permutations

    Apr 13, 2026Emanuele Borgonovo, Francesco Cappelli, Xuefei Lu +2Feature AttributionPerturbation-Based Feature Attribution

    Department of Decision Sciences and Bocconi Institute for Data Science and Analytics, Bocconi University, Milan, Italy · Department of Decision Science, Bocconi University, Milan, Italy · SKEMA Business School, Université Côte d’Azur, France +2

  4. Regional Explanations: Bridging Local and Global Variable Importance

    Apr 13, 2026Salim I. Amoukou, Nicolas J-B. BrunelFeature AttributionExplainable Artificial Intelligence

    J.P. Morgan AI Research · LaMME, ENSIIE, University Paris Saclay & Capgemini Invent

  5. ShapShift: Explaining Model Prediction Shifts with Subgroup Conditional Shapley Values

    Apr 13, 2026Tom Bewley, Salim I. Amoukou, Emanuele Albini +2Shapley Value AttributionFeature Attribution

    J.P. Morgan AI Research

  6. Diffusion-CAM: Faithful Visual Explanations for dMLLMs

    Apr 13, 2026Haomin Zuo, Yidi Li, Luoxiao Yang +1Multimodal Model InterpretabilityFeature Attribution

    Sun Yat-sen University · Northwestern University · Technion - Israel Institute of Technology +1

  7. Investigating Trustworthiness of Nonparametric Deep Survival Models for Alzheimer's Disease Progression Analysis

    Apr 10, 2026Jacob Thrasher, Kaitlyn Heintzelman, Peter Martone +4Alzheimer's Disease Progression PredictionAlgorithmic Fairness

    West Virginia University Morgantown, USA · University of Aberdeen Aberdeen, Scotland

  8. ADAG: Automatically Describing Attribution Graphs

    Apr 8, 2026Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt +1Mechanistic InterpretabilityCircuits in Language Models

    Stanford University · Transluce

  9. Non-identifiability of Explanations from Model Behavior in Deep Networks of Image Authenticity Judgments

    Apr 8, 2026Icaro Re Depaolini, Uri HassonExplainability EvaluationFeature Attribution

    Center for Mind/Brain Sciences, The University of Trento, Trento, Italy

  10. The Attribution Impossibility: No Feature Ranking Is Faithful, Stable, and Complete Under Collinearity

    Apr 8, 2026Drake Caraker, Bryan Arnold, David RhoadsFeature AttributionSHAP Feature Attribution

    Independent Researchers

  11. Learn to Rank: Visual Attribution by Learning Importance Ranking

    Apr 7, 2026David Schinagl, Christian Fruhwirth-Reisinger, Alexander Prutsch +2Feature AttributionExplainable Image Classification

    Institute of Visual Computing (IVC), Graz University of Technology, Graz, Austria · Nomadic AI, USA · Amazon, USA

  12. Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models

    Apr 7, 2026Umesh Biswas, Shafqat Hasan, Syed Mohammed Farhan +2Explainable Intrusion DetectionLLMs for Cybersecurity

    Computer Science & Engineering, Mississippi State University

  13. 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

  14. TTE-CAM: Self-Explainable Class Activation Maps for Pretrained Black-Box CNNs

    Mar 27, 2026Kerol Djoumessi, Philipp BerensExplainable Image ClassificationNeural Network Interpretability

    Hertie Institute for AI in Brain Health, University of Tübingen, Germany · Tübingen AI Center, University of Tübingen, Germany

  15. End-to-end Feature Alignment: A Simple CNN with Intrinsic Class Attribution

    Mar 26, 2026Parniyan Farvardin, David ChapmanNeural Network InterpretabilityExplainable Image Classification

    University of Miami

  16. Combi-CAM: A Novel Multi-Layer Approach for Explainable Image Geolocalization

    Mar 25, 2026David Faget, José Luis Lisani, Miguel ColomFeature AttributionGradient-Based Attribution

    CB · Centre Borelli, ENS Paris-Saclay, Université de Paris, CNRS, INSERM, SSA, France · CB, CMLA +1

  17. Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion

    Mar 23, 2026Abu Noman Md Sakib, OFM Riaz Rahman Aranya, Kevin Desai +1Feature AttributionExplainability Evaluation

    The University of Texas at San Antonio

  18. CLT-Forge: A Scalable Library for Cross-Layer Transcoders and Attribution Graphs

    Mar 22, 2026Florent Draye, Vedant Palit, Abir Harrasse +8LLM InterpretabilityMechanistic Interpretability

    Max Planck Institute for Intelligent Systems, Tübingen, Germany · Jinesis AI Lab, University of Toronto & Vector Institute · CMU +2

  19. Shift-Invariant Feature Attribution in the Application of Wireless Electrocardiograms

    Mar 20, 2026Yalemzerf Getnet, Abiy Tasissa, Waltenegus DargieFeature AttributionElectrocardiography

    Department of Electrical and Computer Engineering at Addis Ababa University, Ethiopia · Department of Mathematics, Tufts University, USA · Faculty of Computer Science, Technische Universität Dresden, 01062 Dresden, Germany

  20. Ensembles-based Feature Guided Analysis

    Mar 20, 2026Federico Formica, Stefano Gregis, Andrea Rota +3Neural Network InterpretabilityFeature Attribution

    McMaster University, Canada · University of Bergamo, Italy

  21. Attribution Upsampling should Redistribute, Not Interpolate

    Mar 17, 2026Vincenzo Buono, Peyman Sheikholharam Mashhadi, Mahmoud Rahat +2Feature AttributionSaliency Map Evaluation

    Halmstad University, Halmstad, Sweden

  22. Hypothesis Class Determines Explanation: Why Accurate Models Disagree on Feature Attribution

    Mar 16, 2026Thackshanaramana BFeature AttributionExplainability Evaluation

    SRM Institute of Science and Technology, India

  23. GradCFA: A Hybrid Gradient-Based Counterfactual and Feature Attribution Explanation Algorithm for Local Interpretation of Neural Networks

    Mar 16, 2026Jacob Sanderson, Hua Mao, Wai Lok WooCounterfactual ExplanationsNeural Network Interpretability

    Department of Computer and Information Sciences, Northumbria University, Newcastle, UK, NE1 8ST

  24. xplainfi: Feature Importance and Statistical Inference for Machine Learning in R

    Mar 16, 2026Lukas Burk, Fiona Katharina Ewald, Giuseppe Casalicchio +2Feature AttributionPerturbation-Based Feature Attribution

  25. Aumann-SHAP: The Geometry of Counterfactual Interaction Explanations in Machine Learning

    Mar 14, 2026Adam Belahcen, Stéphane MussardFeature AttributionShapley Value Attribution

    AIRESS & UM6P

  26. Causal Attribution via Activation Patching

    Mar 13, 2026Amirmohammad Izadi, Mohammadali Banayeeanzade, Alireza Mirrokni +4Feature AttributionTransformer Interpretability

    Sharif University of Technology

  27. Trust Oriented Explainable AI for Fake News Detection

    Mar 12, 2026Krzysztof Siwek, Daniel Stankowski, Maciej StodolskiFake News DetectionExplainable Artificial Intelligence

    Warsaw University of Technology, Warsaw, Poland · Student of Warsaw University of Technology, Warsaw, Poland

  28. What is Missing? Explaining Neurons Activated by Absent Concepts

    Mar 10, 2026Robin Hesse, Simone Schaub-Meyer, Janina Hesse +2Feature VisualizationNeural Network Interpretability

    Max Planck Institute for Informatics, SIC · Department of Computer Science, Technical University of Darmstadt · Leibniz Institute for Resilience Research +2

  29. Causal Interpretation of Neural Network Computations with Contribution Decomposition

    Mar 6, 2026Joshua Brendan Melander, Zaki Alaoui, Shenghua Liu +2Neural Network InterpretabilityMechanistic Interpretability

    Department of Neurobiology, Stanford University · Department of Physics, Stanford University · Department of Applied Physics, Stanford University

  30. Fusion-CAM: Integrating Gradient and Region-Based Class Activation Maps for Robust Visual Explanations

    Mar 5, 2026Hajar Dekdegue, Moncef Garouani, Josiane Mothe +1Explainable Image ClassificationFeature Attribution

    IRIT, UMR5505 CNRS Universit´e de Toulouse · IRIT, UMR5505 CNRS Universit´e Toulouse Capitole · INRAE, Centre Occitanie-Toulouse Unit´e Exp´erimentale APC