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Interpretable Machine Learning: A Comprehensive Review of Foundations, Methods, and the Path Forward
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Interactively Assessing Disentanglement in GANs
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Decision Boundary Visualization for Counterfactual Reasoning
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USTNet: Unsupervised Shape‐to‐Shape Translation via Disentangled Representations
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Generative Deformable Radiance Fields for Disentangled Image Synthesis of Topology‐Varying Objects
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State of the Art of Visual Analytics for eXplainable Deep Learning
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DeforestVis: Behaviour Analysis of Machine Learning Models with Surrogate Decision Stumps
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CAN: Concept‐Aligned Neurons for Visual Comparison of Deep Neural Network Models
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Exploring Classifiers with Differentiable Decision Boundary Maps
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Disentangled Lifespan Synthesis via Transformer‐Based Nonlinear Regression
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Cost‐sensitive tree SHAP for explaining cost‐sensitive tree‐based models
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Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images
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Taming the Triangle: On the Interplays Between Fairness, Interpretability, and Privacy in Machine Learning
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Practical guide to SHAP analysis: Explaining supervised machine learning model predictions in drug development
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An interpretable machine learning methodology to generate interaction effect hypotheses from complex datasets
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Enhancing Psychologists' Understanding Through Explainable Deep Learning Framework for ADHD Diagnosis
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Explainable Artificial Intelligence for Business and Economics: Methods, Applications and Challenges
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Securing Autonomous Vehicles: An In ‐Depth Review of Cyber Attacks and Anomaly Detection Challenges
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A Method‐Oriented Review of Explainable Artificial Intelligence for Neurological Medical Imaging
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Explaining 3D Semantic Segmentation Through Generative AI ‐Based Counterfactuals
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Exploration of materials fatigue influence factors using interpretable machine learning
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Developing Nurse‐Accessible Hypertension Prediction Tools for Low‐Income Populations: A Comparative Analysis of Machine Learning Algorithms With SHAP Interpretation
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Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach
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A sociotechnical view of algorithmic fairness
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Clinical Decision Rules "in the Real World": How a Widely Disseminated Rule Is Used in Everyday Practice
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The use of annotations to explain labels: Comparing results from a human‐rater approach to a deep learning approach
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Using Hierarchical Logistic Regression to Study DIF and DIF Variance in Multilevel Data
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Evaluating the Consistency and Reliability of Attribution Methods in Automated Short Answer Grading (ASAG) Systems: Toward an Explainable Scoring System
inferredcites →https://doi.org/10.1111/jep.12895openalex-snapshot
Transforming clinical practice guidelines and clinical pathways into fast‐and‐frugal decision trees to improve clinical care strategies
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Deep learning and explainable artificial intelligence for investigating dental professionals' satisfaction with CAD software performance
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Asymptotic Distribution of the Bias Corrected Least Squares Estimators in Measurement Error Linear Regression Models Under Long Memory
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Deciphering the noisy landscape: Architectural conceptual design space interpretation using disentangled representation learning
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Data‐driven model for seismic assessment, design, and retrofit of structures using explainable artificial intelligence
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Interpretable Machine Learning Using Partial Linear Models*
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