Interpretable ML

ML: Machine Learning

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

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

An interpretable prototype parts-based neural network for medical tabular data
Mar 5, 2026Jacek Karolczak, Jerzy StefanowskiInterpretable ML Prototype-Based Classification
Poznan University of Technology, Institute of Computing Science, ul. Piotrowo 2, 60-695 Poznań, Poland
Detecting Structural Heart Disease from Electrocardiograms via a Generalized Additive Model of Interpretable Foundation-Model Predictors
Mar 3, 2026Ya Zhou, Zhaohong Sun, Tianxiang Hao +1Electrocardiogram Classification Electrocardiography
Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100037, China · The Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, 518055, China · National Clinical Research Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, National Center for Cardiovascular Diseases, Beijing, 100037, China
Generalizing Logic-based Explanations for Machine Learning Classifiers via Optimization
Mar 2, 2026Francisco Mateus Rocha Filho, Ajalmar Rêgo da Rocha Neto, Thiago Alves RochaExplainable Artificial Intelligence Interpretable ML
Instituto Federal de Educa¸c˜ao, Ciˆencia e Tecnologia do Cear´a (IFCE), Fortaleza, Brazil
GlassMol: Interpretable Molecular Property Prediction with Concept Bottleneck Models
Mar 1, 2026Oscar Rivera, Ziqing Wang, Matthieu Dagommer +2Interpretable ML Molecular Property Prediction
Northwestern University, Evanston, IL 60201, USA · AbbVie, Chicago, IL 60064, USA
Hierarchical Concept-based Interpretable Models
Feb 27, 2026Oscar Hill, Mateo Espinosa Zarlenga, Mateja JamnikInterpretable ML Concept-Based Explanations
University of Cambridge · University of Oxford
ReasonX: Declarative Reasoning on Explanations
Feb 27, 2026Laura State, Salvatore Ruggieri, Franco TuriniExplainable Artificial Intelligence Interpretable ML
University of Pisa, IT · Scuola Normale Superiore, IT · Alexander von Humboldt Institute for Internet and Society, GER
On the Limits of Interpretable Machine Learning in Quintic Root Classification
Feb 26, 2026Rohan Thomas, Majid Bani-YaghoubInterpretable ML Explainability Evaluation
Division of Computing, Analytics & Mathematics, School of Science and Engineering, University of Missouri-Kansas City, 5100 Rockhill Rd., Kansas City, Missouri 64110, USA
Global Interpretability via Automated Preprocessing: A Framework Inspired by Psychiatric Questionnaires
Feb 26, 2026Eric V. StroblInterpretable ML Clinical Outcome Prediction
Department of Biomedical Informatics, University of Pittsburgh.
PIME: Prototype-based Interpretable MCTS-Enhanced Brain Network Analysis for Disorder Diagnosis
Feb 24, 2026Kunyu Zhang, Yanwu Yang, Jing Zhang +2Brain Network Analysis Interpretable ML
Zhengzhou University, Zhengzhou, China · University Hospital Tübingen, Tübingen, Germany · China University of Geosciences, Wuhan, China +3
Behavior Learning (BL): Learning Hierarchical Optimization Structures from Data
Feb 23, 2026Zhenyao Ma, Yue Liang, Dongxu LiInterpretable ML Learning to Optimize
Xiamen University · University of Tübingen · Xi’an Jiaotong University
Provably Explaining Neural Additive Models
Feb 19, 2026Shahaf Bassan, Yizhak Yisrael Elboher, Tobias Ladner +4Interpretable ML Neural Network Interpretability
Hebrew University of Jerusalem, Israel · Technical University of Munich, Germany · Masaryk University, Czech Republic
A feature-stable and explainable machine learning framework for trustworthy decision-making under incomplete clinical data
Feb 19, 2026Justyna Andrys-Olek, Paulina Tworek, Luca Gherardini +4Interpretable ML Learning with Missing Data
Computational Intelligence, Sano Centre for Computational Personalised Medicine, Krakow, Poland · Clinical Studies Group, Randox Laboratories Ltd., Co., Antrim, United Kingdom
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Feb 18, 2026Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos +5Interpretable ML Feature Interaction Modeling
ATHENA RC, Greece · HUA, Greece · Max Planck ISS, Germany +1
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Tabular Prediction
Feb 10, 2026Zizheng Zhang, Yiming Li, Justin Xu +6Interpretable ML Clinical Prediction
Microsoft Research · University of Oxford
KANFIS: A Neuro-Symbolic Framework for Interpretable and Uncertainty-Aware Learning
Feb 3, 2026Binbin Yong, Haoran Pei, Jun Shen +3Interpretable ML Fuzzy Logic
School of Information Science and Engineering, Lanzhou University, China · School of Computing and Information Technology, University of Wollongong, Australia · Department of Data Science and Artificial Intelligence, Monash University, Australia
Explainability of Complex AI Models with Correlation Impact Ratio
Jan 10, 2026Poushali Sengupta, Rabindra Khadka, Sabita Maharjan +5Feature Attribution Interpretable ML
Institute of Informatics, University of Oslo, Oslo, Norway · Department of Computer Science, Oslo Metropolitan University, Oslo, Norway · Aalborg University, Denmark
Causal inference and model explainability analysis of a real world sales dataset
Dec 14, 2025Pranav Gupta, Nithin SurendranCausal Effect Estimation Interpretable ML
Lowe’s Companies Inc, Mooresville, NC, USA
Softly Symbolifying Kolmogorov-Arnold Networks
Nov 27, 2025James Bagrow, Josh BongardKolmogorov-Arnold Networks Interpretable ML
Mathematics & Statistics, University of Vermont, Burlington, VT, United States · Vermont Complex Systems Center, University of Vermont, Burlington, VT, United States · Computer Science, University of Vermont, Burlington, VT, United States
Synthesis and Verification of Register Automata over Data Sequences (Technical Report)
Nov 24, 2025Chih-Duo Hong, Hongjian Jiang, Anthony W. Lin +3Formal Verification Interpretable ML
National Chengchi University, Taipei, Taiwan · University of Kaiserslautern-Landau, Kaiserslautern, Germany · MPI-SWS, Kaiserslautern, Germany +1
Towards Scalable Meta-Learning of near-optimal Interpretable Models via Synthetic Model Generations
Nov 6, 2025Kyaw Hpone Myint, Zhe Wu, Alexandre G. R. Day +1Decision Tree Learning Meta-Learning
Capital One
Making Interpretable Discoveries from Unstructured Data: A High-Dimensional Multiple Hypothesis Testing Approach
Nov 3, 2025Jacob CarlsonSelective Inference Interpretable ML
Harvard University∗
A Theory of the Mechanics of Information: Generalization Through Measurement of Uncertainty (Learning is Measuring)
Oct 26, 2025Christopher J. Hazard, Michael Resnick, Jacob Beel +7Interpretable ML
Howso Incorporated
Interpretable Nanoporous Materials Design with Symmetry-Aware Networks
Sep 19, 2025Zhenhao Zhou, Salman Bin Kashif, Jin-Hu Dou +4Interpretable ML Materials Property Prediction
Center of Hydrogen Science and School of Materials Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China · Innovation Center for Future Materials, Zhangjiang Institute for Advanced Study, Shanghai Jiao Tong University, Shanghai 201203, China · Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States +3
Integrated Multivariate Segmentation Tree for Heterogeneous Credit Data Analysis in Small- and Medium-Sized Enterprises
Aug 30, 2025Lu Han, Xiuying WangFinancial Services Interpretable ML
School of Management Science and Engineering, Central University of Finance and Economics, Beijing 100081, China · School of Computer Science, University of Sydney, NSW 2006, Australia
Learning Interpretable Differentiable Logic Networks for Time-Series Classification
Aug 24, 2025Chang Yue, Niraj K. JhaTime Series Classification Interpretable ML
Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ 08544, USA
Towards Reasonable Concept Bottleneck Models
Jun 5, 2025Nektarios Kalampalikis, Kavya Gupta, Georgi Vitanov +1Concept Bottleneck Models Interpretable ML
Department of Computer Science Saarland University, Germany
Learning thermodynamic master equations for open quantum systems
Jun 2, 2025Peter Sentz, Stanley Nicholson, Yujin Cho +3Quantum System Identification Interpretable ML
Department of Data and Decision Sciences, Emory University, Atlanta, GA 30322 · 2Division of Applied Mathematics, Brown University, Providence, RI 02912 · 3Lawrence Livermore National Laboratory, Livermore, CA 94550
KnowEEG: Explainable Knowledge Driven EEG Classification
May 1, 2025Amarpal Sahota, Navid Mohammadi Foumani, Raul Santos-Rodriguez +1Electroencephalography EEG Decoding
University of Bristol, Bristol, United Kingdom · Monash University, Melbourne, Victoria, Australia
Explainable Boosting Machine for Predicting Claim Severity and Frequency in Car Insurance
Mar 27, 2025Markéta Krúpová, Nabil Rachdi, Quentin GuibertInterpretable ML
CEREMADE · 1ADDACTIS France, 46 bis Chemin du Vieux Moulin, 69160 Tassin-la-Demi-Lune, France · 2CEREMADE, Universit´e Paris-Dauphine, PSL University, CNRS, 75016 Paris, France
Experiments with Optimal Model Trees
Mar 17, 2025Sabino Francesco Roselli, Eibe FrankInterpretable ML Decision Tree Learning
Department of Electrical Engineering, Chalmers University of Technology, Gothenburg, Sweden. · Department of Computer Science, University of Waikato, Hamilton, New Zealand.

Mar 5, 2026J/K move · Enter open · S save

An interpretable prototype parts-based neural network for medical tabular data

Jacek Karolczak, Jerzy Stefanowski

Poznan University of Technology, Institute of Computing Science, ul. Piotrowo 2, 60-695 Poznań, Poland

Interpretable ML Prototype-Based Classification Tabular ML Prototype-Based Learning Clinical Decision Support Clinical Prediction

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The ability to interpret machine learning model decisions is critical in such domains as healthcare, where trust in model predictions is as important as their accuracy. Inspired by the development of prototype parts-based deep neural networks in computer vision, we propose a new model for tabular data, specifically tailored to medical records, that requires discretization of diagnostic result norms. Unlike the original vision models that rely on the spatial structure, our method employs trainable patching over features describing a patient, to learn meaningful prototypical parts from structured data. These parts are represented as binary or discretized feature subsets. This allows the model to express prototypes in human-readable terms, enabling alignment with clinical language and case-based reasoning. Our proposed neural network is inherently interpretable and offers interpretable concept-based predictions by comparing the patient's description to learned prototypes in the latent space of the network. In experiments, we demonstrate that the model achieves classification performance competitive to widely used baseline models on medical benchmark datasets, while also offering transparency, bridging the gap between predictive performance and interpretability in clinical decision support.