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.

Concept frustration: Aligning human concepts and machine representations
Mar 31, 2026Enrico Parisini, Christopher J. Soelistyo, Ahab Isaac +2Interpretable ML
Comprehensive Cancer Centre, Guy’s Hospital, King’s College London, Great Maze Pond, London, SE1 9RT, UK · PharosAI, King’s College London, London, SE1 9RT, UK · The Francis Crick Institute, 1 Midland Rd, London, NW1 1AT, UK +2
Constructing Composite Features for Interpretable Music-Tagging
Mar 30, 2026Chenhao Xue, Weitao Hu, Joyraj Chakraborty +5Music Information Retrieval Automated Feature Engineering
University of Oxford · Independent Researcher · University of Toronto +1
Detecting low left ventricular ejection fraction from ECG using an interpretable and scalable predictor-driven framework
Mar 30, 2026Ya Zhou, Tianxiang Hao, Ziyi Cai +5Left Ventricular Ejection Fraction Estimation Electrocardiography
Department of Information Center, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China · The Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Institute of Statistics and Big Data, Renmin University of China, Beijing, China +2
Process-Aware AI for Rainfall-Runoff Modeling: A Mass-Conserving Neural Framework with Hydrological Process Constraints
Mar 26, 2026Mohammad A. Farmani, Hoshin V. Gupta, Ali Behrangi +3Interpretable ML Physics-Informed ML
Department of Hydrology and Atmospheric Sciences, University of Arizona, Tucson, AZ, USA · Department of Geosciences, University of Arizona, Tucson, AZ, USA
When Consistency Becomes Bias: Interviewer Effects in Semi-Structured Clinical Interviews
Mar 25, 2026Hasindri Watawana, Sergio Burdisso, Diego A. Moreno-Galván +4Depression Detection Interpretable ML
Idiap Research Institute, Switzerland · EPFL, Switzerland · Centro de Investigación en Matemáticas (CIMAT), Mexico +1
Symbolic--KAN: Kolmogorov-Arnold Networks with Discrete Symbolic Structure for Interpretable Learning
Mar 25, 2026Salah A Faroughi, Farinaz Mostajeran, Amirhossein Arzani +1Interpretable ML Scientific ML
Energy & Intelligence Lab, Department of Chemical Engineering, University of Utah, Salt Lake City, Utah, 84112, USA · Department of Mechanical Engineering, University of Utah, Salt Lake City, Utah, 84112, USA · Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, Utah, 84112, USA +2
On the use of Aggregation Operators to improve Human Identification using Dental Records
Mar 24, 2026Antonio D. Villegas-Yeguas, Guillermo R-García, Tzipi Kahana +4Biometric Identification Rank Aggregation
Department of Computer Science and Artificial Intelligence, University of Granada, Granada, Spain · Panacea Cooperative Research S. Coop, Ponferrada, Spain · Institute of Criminology, The Faculty of Law, Hebrew University of Jerusalem, Jerusalem, Israel +4
ShapDBM: Exploring Decision Boundary Maps in Shapley Space
Mar 23, 2026Luke Watkin, Daniel Archambault, Alex TeleaDecision Boundary Analysis Interpretable ML
School of Computing, Newcastle University, UK · Department of Information and Computing Science, Utrecht University, Netherlands
Enhancing LIME using Neural Decision Trees
Mar 21, 2026Mohamed Aymen Bouyahia, Argyris KalogeratosInterpretable ML Explainable Artificial Intelligence
ENS Paris-Saclay, Centre Borelli, CNRS, Gif-Sur-Yvette, France · Le Crédit Lyonnais, Paris, France
Interpretable Battery Aging without Extra Tests via Neural-Assisted Physics-based Modelling
Mar 21, 2026Yuan Qiu, Wei Li, Wei Zhang +4Lithium-Ion Batteries Physics-Informed ML
Singapore Institute of Technology (SIT), Singapore, 828608 · Singapore University of Social Sciences (SUSS), Singapore, 599494 · Institute of Materials Research and Engineering (IMRE), Agency for Science, Technology and Research (A*STAR), 2 Fusionopolis Way, Innovis #08-03, Singapore 138634, Republic of Singapore
Interpretable Multiple Myeloma Prognosis with Observational Medical Outcomes Partnership Data
Mar 20, 2026Salma Rachidi, Aso Bozorgpanah, Eric Fey +1Interpretable ML Cancer Survival Prediction
Aalto University, Dept. of Computer Science, Espoo, Finland · Helsinki University Hospital, Helsinki, Finland
From Concepts to Judgments: Interpretable Image Aesthetic Assessment
Mar 18, 2026Xiao-Chang Liu, Johan WagemansConcept-Based Explanations Interpretable ML
Research Unit Brain and Cognition, KU Leuven Leuven, Belgium
Domain-informed explainable boosting machines for trustworthy lateral spread predictions
Mar 17, 2026Cheng-Hsi Hsiao, Krishna Kumar, Ellen M. RathjeInterpretable ML Physics-Informed ML
Understanding Cell Fate Decisions with Temporal Attention
Mar 17, 2026Florian Bürger, Martim Dias Gomes, Adrián E. Granada +2Microscopy Image Analysis Interpretable ML
Institute for Biomedical Informatics, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany · Faculty of Mathematics and Natural Sciences, University of Cologne, Cologne, Germany · Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany +2
Explainable machine learning workflows for radio astronomical data processing
Mar 17, 2026S. Yatawatta, A. Ahmadi, B. Asabere +3Radio Astronomy Interpretable ML
ASTRON, The Netherlands Institute for Radio Astronomy, Dwingeloo, The Netherlands
Interpretative Interfaces: Designing for AI-Mediated Reading Practices and the Knowledge Commons
Mar 16, 2026Gabrielle BenabdallahLLM Interpretability Interpretable ML
University of Washington Seattle, Washington, USA
Bridging Theory and Data: Correcting Nuclear Mass Models with Interpretable Machine Learning
Mar 16, 2026Yanhua Lu, Tianshuai Shang, Pengxiang Du +2Interpretable ML Scientific ML
College of Physics, Jilin University, Changchun 130012, China. · Department of Physics, Graduate School of Science, The University of Tokyo, Tokyo 113-0033, Japan · RIKEN Interdisciplinary Theoretical and Mathematical Sciences Program, Wako 351-0198, Japan
Concisely Explaining the Doubt: Minimum-Size Abductive Explanations for Linear Models with a Reject Option
Mar 14, 2026Gleilson Pedro Fernandes, Thiago Alves RochaAbductive Reasoning Interpretable ML
Instituto Federal do Ceará (IFCE), Brazil
Close to Reality: Interpretable and Feasible Data Augmentation for Imbalanced Learning
Mar 14, 2026Matheus Camilo da Silva, Gabriel Gustavo Costanzo, Andrea de Lorenzo +1Data Augmentation Synthetic Data Augmentation
Department of Mathematics, Computer Science and Geosciences, University of Trieste, Piazzale Europa, 1, 34127 Trieste TS, Italy.
LingoMotion: An Interpretable and Unambiguous Symbolic Representation for Human Motion
Mar 13, 2026Yao Zhang, Zhuchenyang Liu, Yu XiaoMotion Representation Learning Human Motion Understanding
Aalto University, Espoo, Finland
Explainable AI Using Inherently Interpretable Components for Wearable-based Health Monitoring
Mar 13, 2026Maurice Kuschel, Solveig Vieluf, Claus Reinsberger +2Explainable Time Series Forecasting Explainable Artificial Intelligence
Signal and System Theory Group, Paderborn University, Paderborn, Germany · Department of Medicine I, Ludwig Maximilian University, Munich, Germany · Konrad Zuse School of Excellence in Reliable AI, Munich, Germany +3
Interpreting Contrastive Embeddings in Specific Domains with Fuzzy Rules
Mar 12, 2026Javier Fumanal-Idocin, Mohammadreza Jamalifard, Javier Andreu-PerezInterpretable ML Clinical NLP
School of Computer Science and Electronic Engineering, University of Essex, Colchester, United Kingdom
Teleodynamic Learning a new Paradigm For Interpretable AI
Mar 11, 2026Enrique ter Horst, Juan Diego ZambranoAdaptive Learning Interpretable ML
Interpretable Chinese Metaphor Identification via LLM-Assisted MIPVU Rule Script Generation: A Comparative Protocol Study
Mar 11, 2026Weihang Huang, Mengna LiuFigurative Language Understanding Interpretable ML
University of Birmingham Birmingham, UK · Guangdong University of Foreign Studies Guangzhou, China
Learning Tree-Based Models with Gradient Descent
Mar 11, 2026Sascha MartonStraight-Through Estimator Decision Tree Learning
UNIVERSITY OF MANNHEIM
Digging Deeper: Learning Multi-Level Concept Hierarchies
Mar 10, 2026Oscar Hill, Mateo Espinosa Zarlenga, Mateja JamnikInterpretable ML Concept Learning
Department of Computer Science and Technology, University of Cambridge, UK · Department of Computer Science, University of Oxford, UK
TT-Sparse: Learning Sparse Rule Models with Differentiable Truth Tables
Mar 8, 2026Hans Farrell Soegeng, Sarthak Ketanbhai Modi, Thomas PeyrinBoolean Function Learning Interpretable ML
School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore.
Explainable and Hardware-Efficient Jamming Detection for 5G Networks Using the Convolutional Tsetlin Machine
Mar 7, 2026Vojtech Halenka, Mohammadreza Amini, Per-Arne Andersen +2Wireless Communications Edge Inference
Center of Artificial Intelligence Research, University of Agder, Grimstad, Norway · School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada
Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation
Mar 6, 2026Julien Amblard, Niklas Groll, Matthew Tait +3Fault Detection Interpretable ML
Department of Computing, Imperial College London, London, United Kingdom · Department of Chemical and Biochemical Engineering, Technical University of Denmark, Kongens Lyngby, Denmark · ILASP Limited, London, United Kingdom
Who We Are, Where We Are: Mental Health at the Intersection of Person, Situation, and Large Language Models
Mar 6, 2026Nikita Soni, August Håkan Nilsson, Syeda Mahwish +3Interpretable ML Computational Social Science
Department of Computer Science, Stony Brook University · Oslo Metropolitan University, Oslo Business School · University of Texas at Dallas, Dept. of Psychology

Mar 31, 2026J/K move · Enter open · S save

Concept frustration: Aligning human concepts and machine representations

Enrico Parisini, Christopher J. Soelistyo, Ahab Isaac, Alessandro Barp+1

Comprehensive Cancer Centre, Guy’s Hospital, King’s College London, Great Maze Pond, London, SE1 9RT, UK · PharosAI, King’s College London, London, SE1 9RT, UK · The Francis Crick Institute, 1 Midland Rd, London, NW1 1AT, UK+2

Interpretable ML

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Aligning human-interpretable concepts with the internal representations learned by modern machine learning systems remains a central challenge for interpretable AI. We introduce a geometric framework for comparing supervised human concepts with unsupervised intermediate representations extracted from foundation model embeddings. Motivated by the role of conceptual leaps in scientific discovery, we formalise the notion of concept frustration: a contradiction that arises when an unobserved concept induces relationships between known concepts that cannot be made consistent within an existing ontology. We develop task-aligned similarity measures that detect concept frustration between supervised concept-based models and unsupervised representations derived from foundation models, and show that the phenomenon is detectable in task-aligned geometry while conventional Euclidean comparisons fail. Under a linear-Gaussian generative model we derive a closed-form expression for Bayes-optimal concept-based classifier accuracy, decomposing predictive signal into known-known, known-unknown and unknown-unknown contributions and identifying analytically where frustration affects performance. Experiments on synthetic data and real language and vision tasks demonstrate that frustration can be detected in foundation model representations and that incorporating a frustrating concept into an interpretable model reorganises the geometry of learned concept representations, to better align human and machine reasoning. These results suggest a principled framework for diagnosing incomplete concept ontologies and aligning human and machine conceptual reasoning, with implications for the development and validation of safe interpretable AI for high-risk applications.