Interpretable ML

ML: Machine Learning

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Overview

Interpretable machine learning aims to make predictions understandable enough for people to inspect, challenge, and use, especially in high-stakes decisions and scientific discovery. Approaches include inherently transparent decision trees, rule sets, additive models, and symbolic formulas, as well as concept- and prototype-based models that connect predictions to human-readable ideas or examples. Other methods explain complex models after training with feature attributions, local surrogates, or analyses of internal representations, while newer work also builds in uncertainty, domain knowledge, and opportunities for human intervention. A central challenge is balancing predictive accuracy with explanations that are faithful, stable under changes in data, and meaningful to the people relying on them; evaluation increasingly tests these qualities rather than treating a plausible explanation as sufficient.

Latest papers

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310 papers26 papers in the last four weeks, level with the four weeks before. 0.2% of all new papers.

  1. Interpretable Quantile Regression by Optimal Decision Trees

    Apr 22, 2026Valentin Lemaire, Gaël Aglin, Siegfried NijssenQuantile RegressionInterpretable ML

    Euranova, Rue Emile Francqui, Mont-Saint-Guibert, Belgium · Université Catholique de Louvain, Louvain-la-Neuve, Belgium

  2. Improving clinical interpretability of linear neuroimaging models through feature whitening

    Apr 22, 2026Sara Petiton, Antoine Grigis, Raphaël Vock +1Medical DiagnosisFeature Whitening

    University Paris-Saclay, CEA, CNRS, NeuroSpin, Baobab UMR 9027, Saclay, France

  3. Mechanistic Interpretability Tool for AI Weather Models

    Apr 22, 2026Kirsten I. Tempest, Matthias Beylich, George C. CraigInterpretable MLMechanistic Interpretability

    Meteorological Institute Munich, Ludwig-Maximilians-Universität, 80333 Munich, Germany · Deutsches Zentrum für Luft- und Raumfahrt, Oberpfaffenhofen, Germany

  4. Clinically Interpretable Sepsis Early Warning via LLM-Guided Simulation of Temporal Physiological Dynamics

    Apr 22, 2026Weizhi Nie, Zhen Qu, Weijie Wang +4Interpretable MLLLM-Based Time Series Forecasting

    School of Electrical and Information Engineering, Tianjin University, Tianjin, 300072, Tianjin, China · Department of Information Engineering and Computer Science, University of Trento, Trento, Trentino, Italy · Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, Jiangsu, China +3

  5. Diagnosable ColBERT: Debugging Late-Interaction Retrieval Models Using a Learned Latent Space as Reference

    Apr 21, 2026François RemyInterpretable MLMulti-Vector Retrieval

    Parallia AI

  6. ParamBoost: Gradient Boosted Piecewise Cubic Polynomials

    Apr 20, 2026Nicolas Salvadé, Tim HillelInterpretable ML

    Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London, WC1E 6BT,, United Kingdom

  7. PRISMA: Preference-Reinforced Self-Training Approach for Interpretable Emotionally Intelligent Negotiation Dialogues

    Apr 20, 2026Prajwal Vijay Kajare, Priyanshu Priya, Bikash Santra +1Automated NegotiationCoT Reasoning

    School of Artificial Intelligence and Data Science, Indian Institute of Technology Jodhpur, India · Department of Computer Science and Engineering, Indian Institute of Technology Patna, India

  8. Federated Rule Ensemble Method in Medical Data

    Apr 20, 2026Ke Wan, Kensuke Tanioka, Toshio ShimokawaInterpretable MLPrivacy-Preserving ML

    Department of Medical Data Science Wakayama Medical University · Department of Biomedical Sciences and Informatics Doshisha University · Department of Biostatistics Wakayama Medical University

  9. Tree of Concepts: Interpretable Continual Learners in Non-Stationary Clinical Domains

    Apr 18, 2026Dongkyu Cho, Xiyue Li, Samrachana Adhikari +1Concept Bottleneck ModelsHealthcare

    Computer Science Department New York University New York, NY, USA · Department of Population Health NYU Grossman School of Medicine New York, NY, USA · School of Global Public Health Computer Science Department New York University New York, NY, USA

  10. Prototype-Grounded Concept Models for Verifiable Concept Alignment

    Apr 17, 2026Stefano Colamonaco, David Debot, Pietro Barbiero +1Concept Bottleneck ModelsConcept Alignment

    Department of Computer Science, KU Leuven · IBM Research, Zurich

  11. Discovering quantum phenomena with Interpretable Machine Learning

    Apr 17, 2026Paulin de Schoulepnikoff, Hendrik Poulsen Nautrup, Hans J. Briegel +1Interpretable MLQuantum Machine Learning

    University of Innsbruck, Department for Theoretical Physics, Technikerstr. 21a, A-6020 Innsbruck, Austria

  12. EVIL: Evolving Interpretable Algorithms for Zero-Shot Inference on Event Sequences and Time Series with LLMs

    Apr 17, 2026David BerghausEvent Time PredictionTime Series Imputation

    Lamarr Institute · Fraunhofer IAIS

  13. An Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules

    Apr 17, 2026Jingke Chen, Jingrui Zhong, Tazneen Hossain Tani +3Protein-Ligand Binding Affinity PredictionInterpretable ML

    MOE Key Laboratory of Bioinformatics, State Key Laboratory of Molecular Oncology, Beijing Frontier Research Center for Biological Structure, School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China

  14. Structural interpretability in SVMs with truncated orthogonal polynomial kernels

    Apr 16, 2026Víctor Soto-Larrosa, Nuria Torrado, Edmundo J. HuertasInterpretable MLSupport Vector Machines

    Dpto. de Biociencias, Facultad de Ciencias Biomédicas y de la Salud, Universidad Europea de Madrid, C/ Tajo, s/n, 28670 Villaviciosa de Odón, Madrid, Spain. · Dpto. de Matemáticas, Facultad de Ciencias, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 7, 28049 Madrid, Spain. · Instituto de Ciencias Matemáticas (ICMAT), Campus de Cantoblanco UAM, C/ Nicolás Cabrera, 13–15, 28049 Madrid, Spain. +1

  15. xFODE: An Explainable Fuzzy Additive ODE Framework for System Identification

    Apr 16, 2026Ertugrul Kececi, Tufan KumbasarInterpretable MLNonlinear System Identification

    AI and Intelligent Systems Laboratory, Istanbul Technical University, 34469, Istanbul, Türkiye

  16. Expert-Guided Class-Conditional Goodness-of-Fit Scores for Interpretable Classification with Informative Missingness

    Apr 16, 2026Shahar Cohen, David M. Steinberg, Yael Radzyner +1Interpretable ML

    Department of Statistics and Operations Research, School of Mathematical Sciences, Tel Aviv University, Tel Aviv, Israel · Israel NDC, Soreq Nuclear Research Center, Yavne, Israel

  17. CI-CBM: Class-Incremental Concept Bottleneck Model for Interpretable Continual Learning

    Apr 16, 2026Amirhosein Javadi, Tuomas Oikarinen, Tara Javidi +1Class-Incremental LearningConcept Bottleneck Models

    Department of Electrical and Computer Engineering, University of California San Diego · Department of Computer Science and Engineering, University of California San Diego · Halıcıoğlu Data Science Institute, University of California San Diego

  18. Enhancing the interpretability of spatially variable N2O model predictions with soft sensors during wastewater treatment

    Apr 15, 2026Mohammad Raeisi Gahrouei, Pedram Ramin, Vincenzo A. Riggio +1Virtual SensingInterpretable ML

    Infrastructure and Environment, School of Engineering, University of Glasgow, University Avenue, Glasgow G12 8QQ, United Kingdom · DIATI, Politecnico di Torino, C.so Duca Degli Abruzzi, 24, 10129, Torino, Italy · PROSYS, Department of Chemical and Biochemical Engineering, Technical University of Denmark, 2800 Kgs., Lyngby, Denmark

  19. LLM-Guided Semantic Bootstrapping for Interpretable Text Classification with Tsetlin Machines

    Apr 14, 2026Jiechao Gao, Rohan Kumar Yadav, Yuangang Li +4Text ClassificationInterpretable ML

    Stanford University · Independent Researcher · University of California, Irvine +1

  20. Interpretable DNA Sequence Classification via Dynamic Feature Generation in Decision Trees

    Apr 13, 2026Nicolas Huynh, Krzysztof Kacprzyk, Ryan Sheridan +2Interpretable MLDecision Tree Learning

    University of Cambridge · CU Anschutz Medical Campus

  21. LaScA: Language-Conditioned Scalable Modelling of Affective Dynamics

    Apr 8, 2026Kosmas Pinitas, Ilias MaglogiannisAffective ComputingMultimodal Emotion Recognition

    Department of Digital Systems, University of Piraeus

  22. Energy-Regularized Spatial Masking: A Novel Approach to Enhancing Robustness and Interpretability in Vision Models

    Apr 8, 2026Tom Devynck, Bilal Faye, Djamel Bouchaffra +3Interpretable MLStructured Sparsity

    DAVID Lab, UVSQ, Paris-Saclay University, Versailles, France · LIPN, UMR CNRS 7030, Sorbonne Paris Nord University, Villetaneuse, France · LISN, Paris-Saclay University, Saclay, France

  23. Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST

    Apr 7, 2026Michael Karnes, Alper YilmazInterpretable MLMedical Image Representation Learning

    The Ohio State University, Columbus, OH 43210, USA

  24. From Large Language Model Predicates to Logic Tensor Networks: Neurosymbolic Offer Validation in Regulated Procurement

    Apr 7, 2026Cedric Haufe, Frieder StolzenburgLogic Tensor NetworksNeuro-Symbolic Reasoning

    Harz University of Applied Sciences, Wernigerode, Germany · Merseburg University of Applied Sciences, Merseburg, Germany · Senior Visiting Fellow, University of New South Wales (UNSW), School of Computer Science and Engineering, Sydney, Australia

  25. The Infinite-Dimensional Nature of Spectroscopy and Why Models Succeed, Fail, and Mislead

    Apr 6, 2026Umberto Michelucci, Francesca VenturiniClassificationInterpretable ML

    Department of Computer Science Lucerne University of Applied Sciences and Arts Risch-Rotkreuz, 6343 Switzerland · Institute of Applied Mathematics and Physics ZHAW Zurich University of Applied Sciences Winterthur, 8400 Switzerland

  26. Explainable Machine Learning for Sepsis Outcome Prediction Using a Novel Romanian Electronic Health Record Dataset

    Apr 6, 2026Andrei-Alexandru Bunea, Ovidiu Ghibea, Dan-Matei Popovici +2Sepsis PredictionClinical Outcome Prediction

    POLITEHNICA Bucharest National University of Science and Technology, Bucharest, Romania · Carol Davila University of Medicine and Pharmacy, Faculty of Medicine, Bucharest, Romania · General Surgery Department, University Emergency Hospital of Bucharest, Bucharest, Romania +1

  27. Interpretation of Crystal Energy Landscapes with Kolmogorov-Arnold Networks

    Apr 6, 2026Gen Zu, Ning Mao, Claudia Felser +1Materials Property PredictionInterpretable ML

    Max Planck Institute for Chemical Physics of Solids, 01187, Dresden, Germany · Department of Physics and Astronomy, University of Tennessee, Knoxville, TN 37996, USA · Min H. Kao Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville, Tennessee 37996, USA

  28. Learning an Interpretable Risk Scoring System for Maximizing Decision Net Benefit

    Apr 5, 2026Wenhao Chi, Ş. İlker BirbilCost-Sensitive LearningInterpretable ML

    Yau Mathematical Sciences Center, Tsinghua University · Amsterdam Business School, University of Amsterdam

  29. Adaptive Alarm Threshold Prediction in 4G Mobile Networks: A Percentile-Guided Deep Learning Framework with Interpretable Outputs

    Apr 4, 2026Ayon Roy, Sadman Sharif, Shiva Prasad SarkarInterpretable ML

  30. PASM: Population Adaptive Symbolic Mixture-of-Experts Model for Cross-location Hurricane Evacuation Decision Prediction

    Mar 31, 2026Xiao Qian, Shangjia DongDomain GeneralizationMixture-of-Experts Models

    Dept. of Civil, Environmental, and Construction Engineering, University of Delaware, Newark, DE, USA