yusuke1997/dblp-id-title huggingface dataset:
Integrated Gradients for Enhanced Interpretation of P3b-ERP Classifiers Trained with EEG-superlets in Traditional and Virtual Environments
Explanation Groves - Controlling the Trade-off between the Degree of Explanation vs. its Complexity
State Graph Based Explanation Approach for Black-Box Time Series Model
A Novel Approach for Benchmarking Local Binary Classification XAI Methods Using Synthetic Ground Truth Datasets
IPEXCO: A Platform for Iterative Planning with Interactive Goal-Conflict Explanations
Explaining the Transfer Learning Ability of a Deep Neural Networks by Means of Representations
Integration of Explainable Deep Neural Network with Blockchain Technology: Medical Indemnity Insurance
Too Sure for Trust. The Paradoxical Effect of Calibrated Confidence in Case of Uncalibrated Trust in Hybrid Decision Making
Temporal Explainable AI Models for Surgery Evaluation
Fostering Human-AI interaction: development of a Clinical Decision Support System enhanced by eXplainable AI and Natural Language Processing
Glocal Explanations of Expected Goal Models in Football
A Novel Structured Argumentation Framework for Improved Explainability of Classification Tasks
Explaining Deep Reinforcement Learning-Based Methods for Control of Building HVAC Systems
Explainable Evaluation of Emotion Recognition with Low-Cost EEG: Feature Engineering and Interpretability Insights
Explaining Model Behavior with Global Causal Analysis
The Dynamics of Explainability: Diverse Insights from SHAP Explanations using Neighbourhoods
An Interactive XAI Interface with Application in Healthcare for Non-experts
Leaking LoRa: An Evaluation of Password Leaks and Knowledge Storage in Large Language Models
Evaluating Local Explainable AI Techniques for the Classification of Chest X-Ray Images
eXplego: An interactive Tool that Helps you Select Appropriate XAI-methods for your Explainability Needs
Diffusion Counterfactuals for Image Regressors
Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making
CausalAIME: Leveraging Peter-Clark Algorithms and Inverse Modeling for Unified Global Feature Explanation in Healthcare
Guiding LLMs to Generate High-Fidelity and High-Quality Counterfactual Explanations for Text Classification
The Impact of Concept Explanations and Interventions on Human-Machine Collaboration
Global Properties from Local Explanations with Concept Explanation Clusters
Necessary and Sufficient Explanations of Multi-Criteria Decision Aiding Models, with and Without Interacting Criteria
Better Luck Next Time: About Robust Recourse in Binary Allocation Problems
Explaining Time Series Classifiers Through Post-Hoc XAI Methods Capturing Temporal Dependencies
Evaluating the Faithfulness of Causality in Saliency-Based Explanations of Deep Learning Models for Temporal Colour Constancy
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in Senegal
Unsupervised and Interpretable Detection of User Personalities in Online Social Networks
Evaluating Neighbor Explainability for Graph Neural Networks
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI
Explaining Clustering of Ecological Momentary Assessment Data Through Temporal and Feature Attention
Finding Spurious Correlations with Function-Semantic Contrast Analysis
Leveraging Group Contrastive Explanations for Handling Fairness
Explainable Artificial Intelligence - Third World Conference, xAI 2025, Istanbul, Turkey, July 9-11, 2025, Proceedings, Part I
ABC-GAN: Spatially Constrained Counterfactual Generation for Image Classification Explanations
Artificial Representative Trees as Interpretable Surrogates for Random Forests
Visual Analysis of Action Policy Behavior: A Case Study in Grid-World Driving
Natural Example-Based Explainability: A Survey
Revitalize the Potential of Radiomics: Interpretation and Feature Stability in Medical Imaging Analyses through Groupwise Feature Importance
From Black Boxes to Conversations: Incorporating XAI in a Conversational Agent
Explainable Artificial Intelligence and Reasoning in the Context of Large Neural Network Models
The Co-12 Recipe for Evaluating Interpretable Part-Prototype Image Classifiers
Explainable Artificial Intelligence - Second World Conference, xAI 2024, Valletta, Malta, July 17-19, 2024, Proceedings, Part I
Evaluating the Explainability of Attributes and Prototypes for a Medical Classification Model
When Can You Trust Your Explanations? A Robustness Analysis on Feature Importances
Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest
On Combining Embeddings, Ontology and LLM to Retrieve Semantically Similar Quranic Verses and Generate Their Explanations
Investigating the Relationship Between Debiasing and Artifact Removal using Saliency Maps
Revisiting FunnyBirds Evaluation Framework for Prototypical Parts Networks
Strategies to Exploit XAI to Improve Classification Systems
Explainability in Generative AI: An Umbrella Review of Current Techniques, Limitations, and Future Directions
XAIEV - A Framework for the Evaluation of XAI-Algorithms for Image Classification
HalCECE: A Framework for Explainable Hallucination Detection Through Conceptual Counterfactuals in Image Captioning
Looking for the Right Paths to Use XAI in the Judiciary. Which Branches of Law Need Inherently Interpretable Machine Learning Models and Why?
Topological Data Analysis for Trustworthy AI
COIN: Counterfactual Inpainting for Weakly Supervised Semantic Segmentation for Medical Images
Explainable Artificial Intelligence in Education: A Comprehensive Review
Explainable Concept Mappings of MRI: Revealing the Mechanisms Underlying Deep Learning-Based Brain Disease Classification
A Comparative Analysis of SHAP, LIME, ANCHORS, and DICE for Interpreting a Dense Neural Network in Credit Card Fraud Detection
A Combination of Integrated Gradients and SRFAMap for Explaining Neural Networks Trained with High-Order Statistical Radiomic Features
XAgent: A Conversational XAI Agent Harnessing the Power of Large Language Models
Identifying EEG Biomarkers of Depression with Novel Explainable Deep Learning Architectures
Deriving and Visualizing Predictive Rules for Disease Risk: A Transparent Approach to Medical AI
Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
Local Interpretable Model-Agnostic Explanations for Multitarget Image Regression
Construction of Artificial Most Representative Trees by Minimizing Tree-Based Distance Measures
Algorithmic Knowability: A Unified Approach to Explanations in the AI Act
Cost of Explainability in AI: An Example with Credit Scoring Models
Do Intermediate Feature Coalitions Aid Explainability of Black-Box Models?
ConformaSegment: A Conformal Prediction-Based, Uncertainty-Aware, and Model-Agnostic Explainability Framework for Time-Series Forecasting
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAI
A Novel Architecture for Robust Explainable AI Approaches in Critical Object Detection Scenarios Based on Bayesian Neural Networks
Comparing XAI Explanations and Synthetic Data Augmentation Strategies in Neuroimaging AI
Sparseness-Optimized Feature Importance
Beyond One-Hot-Encoding: Injecting Semantics to Drive Image Classifiers
Ensuring Safe Social Navigation via Explainable Probabilistic and Conformal Safety Regions
Detecting Fraud in Financial Networks: A Semi-supervised GNN Approach with Granger-Causal Explanations
Concept Bottleneck Model with Emergent Communication Framework for Explainable AI
Relating the Partial Dependence Plot and Permutation Feature Importance to the Data Generating Process
Model agnostic calibration of image classifiers
Lung images Classification with Textural Characteristics and Hybrid Classification Schemes
When Bias Backfires: The Modulatory Role of Counterfactual Explanations on the Adoption of Algorithmic Bias in XAI-Supported Human Decision-Making
Propaganda Detection Robustness Through Adversarial Attacks Driven by eXplainable AI
Stabilizing Estimates of Shapley Values with Control Variates
Uncovering Decision-making Process of Cost-sensitive Tree-based Classifiers using the Adaptation of TreeSHAP
Joint Proceedings of the xAI-2023 Late-breaking Work, Demos and Doctoral Consortium co-located with the 1st World Conference on eXplainable Artificial Intelligence (xAI-2023), Lisbon, Portugal, July 26-28, 2023
Validation of ML Models from the Field of XAI for Computer Vision in Autonomous Driving
When Attention Turn To Be Explanation. A Case Study in Recommender Systems
An Examination of the Effect of the Inconsistency Budget in Weighted Argumentation Frameworks and their Impact on the Interpretation of Deep Neural Networks
Discriminative Feature Analysis in XAI for Multi-Class Classification of Oral Lesions
Explaining Socio-Demographic and Behavioral Patterns of Vaccination Against the Swine Flu (H1N1) Pandemic
CoProNN: Concept-Based Prototypical Nearest Neighbors for Explaining Vision Models
Exploring the Role of Explainable AI in the Development and Qualification of Aircraft Quality Assurance Processes: A Case Study
An Empirical Investigation of Users' Assessment of XAI Explanations: Identifying the Sweet Spot of Explanation Complexity and Value
Ablation Path Saliency
IxDRL: A Novel Explainable Deep Reinforcement Learning Toolkit Based on Analyses of Interestingness
Bridging the Interpretability Gap in Process Mining: A Comprehensive Approach Combining Explainable Clustering and Generative AI
Opening the Black Box: Analyzing Attention Weights and Hidden States in Pre-trained Language Models for Non-language Tasks
Extended Nomological Deductive Reasoning (eNDR) for Transparent AI Outputs
Semantic Meaningfulness: Evaluating Counterfactual Approaches for Real-World Plausibility and Feasibility
iPDP: On Partial Dependence Plots in Dynamic Modeling Scenarios
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features
Interpretable Sexism Detection with Explainable Transformers
AutoCL: AutoML for Concept Learning
Online Explainable Ensemble of Tree Models Pruning for Time Series Forecasting
Reason to Explain: Interactive Contrastive Explanations (REASONX)
Privacy Implications of Explainable AI in Data-Driven Systems
Graph Edits for Counterfactual Explanations: A Comparative Study
Algorithm-Agnostic Feature Attributions for Clustering
Examining the Nexus between Explainability of AI Systems and User's Trust: A Preliminary Scoping Review
Challenges and Opportunities in Text Generation Explainability
Interpretable Machine Learning for TabPFN
Adding Why to What? Analyses of an Everyday Explanation
Federated Learning of Explainable Artificial Intelligence Models for Predicting Parkinson's Disease Progression
Second Glance: A Novel Explainable AI to Understand Feature Interactions in Neural Networks using Higher-Order Partial Derivatives
Feature Importance to Explain Multimodal Prediction Models. a Clinical Use Case
Explainable Machine Learning for Bag of Words-Based Phishing Detection
Towards Evaluation of Explainable Artificial Intelligence in Streaming Data
Legal Requirements, Trust Issues and Engineering Challenges - A Multi-disciplinary Case for User-Specific Explainability
Improving the Weighting Strategy in KernelSHAP
Understanding Unsupervised Learning Explanations Using Contextual Importance and Utility
Explainable Optimization: Leveraging Large Language Models for User-Friendly Explanations
Human-Centered Explainable AI: Creating Explanations that Address Stakeholder Needs
Reasoning-Grounded Natural Language Explanations for Language Models
A Fresh Look at Sanity Checks for Saliency Maps
Design Guidelines for XAI in the Healthcare Domain
Prototype-Based Interpretable Breast Cancer Prediction Models: Analysis and Challenges
Global Concept Explanations for Graphs by Contrastive Learning
Toward Understanding the Disagreement Problem in Neural Network Feature Attribution
An Evaluation of Contextual Importance and Utility for Outcome Explanation of Black-Box Predictions for Medical Datasets
Explain to Gain: Introspective Reinforcement Learning for Enhanced Performance
From Input to Insight: Probing the Reasoning of Attention-Based MIL Models
Interpreting the Structure of Multi-object Representations in Vision Encoders
Unveiling Black-Boxes: Explainable Deep Learning Models for Patent Classification
DExT: Detector Explanation Toolkit
AcME-AD: Accelerated Model Explanations for Anomaly Detection
Knowledge Graphs and Explanations for Improving Detection of Diseases in Images of Grains
Channel Modeling for Millimeter-Wave UAV Communication based on Explainable Generative Neural Network
Post Hoc Explanations for RNNs using State Transition Representations for Time Series Data
Uncertainty Considerations of Explainable AI in Data-Driven Systems
Consolidating Explanation Stability Metrics
SHAP-Guided Regularization in Machine Learning Models
POMELO: Black-Box Feature Attribution with Full-Input, In-Distribution Perturbations
Which Direction to Choose? An Analysis on the Representation Power of Self-Supervised ViTs in Downstream Tasks
Balancing Fairness and Interpretability in Clustering with FairParTree
Can AI Regulate Your Emotions? An Empirical Investigation of the Influence of AI Explanations and Emotion Regulation on Human Decision-Making Factors
Explaining Deep Learning Time Series Classification Models using a Decision Tree-Based Post-Hoc XAI Method
Outcome-Guided Counterfactuals from a Jointly Trained Generative Latent Space
Multi-modal Machine Learning Model for Interpretable Malware Classification
Towards Assurance of LLM Adversarial Robustness using Ontology-Driven Argumentation
PERFEX: Classifier Performance Explanations for Trustworthy AI Systems
Spectral Occlusion - Attribution Beyond Spatial Relevance Heatmaps
Personalized Human-Robot Interaction in Companion Social Robots
Explaining ANN-modeled fMRI Data with Path-Weights and Layer-Wise Relevance Propagation
Explainable Virtual Metrology in Semiconductor Industry
Assessing the Value of Explainable Artificial Intelligence for Magnetic Resonance Imaging
Template Decision Diagrams for Meta Control and Explainability
ClustXRAI: Interactive Cluster Exploration and Explanation for Process Mining with Generative AI
Bridging Structural and Functional Imaging: Integrated PET/CT Radiomics with Explainable Machine Learning
A proposal for improving EEG microstate generation via interpretable deep clustering with convolutional autoencoders
Beyond Model Trust: Dual XAI for Adaptive and User-Centric Explainability
A Prototype of an Interactive Clinical Decision Support System with Counterfactual Explanations
Selecting Textural Characteristics of Chest X-Rays for Pneumonia Lesions Classification with the Integrated Gradients XAI Attribution Method
Post-hoc Concept Disentanglement: From Correlated to Isolated Concept Representations
Perception and Consideration of the Explainees' Needs for Satisfying Explanations
Explaining Graph Classifiers by Unsupervised Node Relevance Attribution
A GUI for the Fair & Explainable Selective Classifier IFAC
Generate Explanations for Time-series classification by ChatGPT
Exploring the Reliability of SHAP Values in Reinforcement Learning
MSL: Multi-class Scoring Lists for Interpretable Incremental Decision-Making
Detecting Concept Drift with SHapley Additive ExPlanations for Intelligent Model Retraining in Energy Generation Forecasting
A Biologically Inspired Filter Significance Assessment Method for Model Explanation
Explainable Artificial Intelligence - First World Conference, xAI 2023, Lisbon, Portugal, July 26-28, 2023, Proceedings, Part I
Exploring Explainability in Federated Learning: A Comparative Study on Brain Age Prediction
Robustness Analysis of Counterfactual Explanations from Generative Models: An Empirical Study
On the Interplay of Explainability, Privacy and Predictive Performance with Explanation-assisted Model Extraction
Counterfactual generating Variational Autoencoder for Anomaly Detection
An Exploration of the Latent Space of a Convolutional Variational Autoencoder for the Generation of Musical Instrument Tones
Dear XAI Community, We Need to Talk! - Fundamental Misconceptions in Current XAI Research
What Will Make Misinformation Spread: An XAI Perspective
Evaluating Self-attention Interpretability Through Human-Grounded Experimental Protocol
Explaining Process Behavior: A Declarative Framework for Interpretable Event Data
Contrastive Visual Explanations for Reinforcement Learning via Counterfactual Rewards
XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users
Optimizing Synthetic Data from Scarcity: Towards Meaningful Data Generation in High-Dimensional Low-Sample Size Domains
Exploring Ensemble Strategies for Graph Counterfactual Explanations
Explainable AI for Mixed Data Clustering
Investigating Neuron Ablation in Attention Heads: The Case for Peak Activation Centering
Understanding the Dependence of Perception Model Competency on Regions in an Image
The Accuracy and Faithfullness of AL-DLIME - Active Learning-Based Deterministic Local Interpretable Model-Agnostic Explanations: A Comparison with LIME and DLIME in Medicine
Metric-Guided Synthesis for Class Activation Mapping
Interpreting Black-Box Time Series Classifiers using Parameterised Event Primitives
Towards Explainable Image Classification
Can Reduction of Bias Decrease the Need for Explainability? Working with Simplified Models to Understand Complexity
Comparative Explanations: Explanation Guided Decision Making for Human-in-the-Loop Preference Selection
Enhancing Cost-Sensitive Tree-Based XAI Surrogate Method: Exploring Alternative Cost Matrix Formulation
An Explainable AI User Interface for Facilitating Collaboration between Domain Experts and AI Researchers
The Importance of Time in Causal Algorithmic Recourse
Explainable Fraud Detection with Deep Symbolic Classification
Explainable Artificial Intelligence Applied to Predictive Maintenance: Comparison of Post-Hoc Explainability Techniques
Integrating GPT-Technologies with Decision Models for Explainability
Latent Space Interpretation and Mechanistic Clipping of Subject-Specific Variational Autoencoders of EEG Topographic Maps for Artefacts Reduction
Interpreting Forecasted Vital Signs Using N-BEATS in Sepsis Patients
Explanations for Medical Diagnosis Predictions Based on Argumentation Schemes
Towards Mechanistic Interpretability for Autoencoder compression of EEG signals
How Much Can Stratification Improve the Approximation of Shapley Values?
Explaining Uncertainty: Exploring the Synergies of Explainable Artificial Intelligence and Uncertainty Quantification
MEGAN: Multi-explanation Graph Attention Network
SkinSplain: An XAI Framework for Trust Calibration in Skin Lesion Analysis
Perlocution vs Illocution: How Different Interpretations of the Act of Explaining Impact on the Evaluation of Explanations and XAI
Local List-Wise Explanations of LambdaMART
Generative Inpainting for Shapley-Value-Based Anomaly Explanation
Weakly Supervised Pixel-Level Annotation with Visual Interpretability
Compare-xAI: Toward Unifying Functional Testing Methods for Post-hoc XAI Algorithms into a Multi-dimensional Benchmark
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability
Superpixel Correlation for Explainable Image Classification
Quantitative Evaluation of xAI Methods for Multivariate Time Series - A Case Study for a CNN-Based MI Detection Model
A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany
Counterfactual Shapley Values for Explaining Reinforcement Learning
CAGE: Causality-Aware Shapley Value for Global Explanations
Transparently Predicting Therapy Compliance of Young Adults Following Ischemic Stroke
XAI In Fraud Detection: A Causal Perspective
Explainable Machine Learning via Argumentation
FAIR-MED: Bias Detection and Fairness Evaluation in Healthcare Focused XAI
Unraveling Anomalies: Explaining Outliers with DTOR
Handling Missing Values in Local Post-hoc Explainability
AnyCBMs: How to Turn Any Black Box into a Concept Bottleneck Model
Model-Agnostic Knowledge Graph Embedding Explanations for Recommender Systems
Unveiling the Anatomy of Adversarial Attacks: Concept-Based XAI Dissection of CNNs
BEExAI: Benchmark to Evaluate Explainable AI
Conditional Calibrated Explanations: Finding a Path Between Bias and Uncertainty
Explaining Search Result Stances to Opinionated People
PFLex: Perturbation-Free Local Explanations in Language Model-Based Text Classification
Closing the Loop: Testing ChatGPT to Generate Model Explanations to Improve Human Labelling of Sponsored Content on Social Media
SAC-FACT: Soft Actor-Critic Reinforcement Learning for Counterfactual Explanations
Accelerating Implementation of Artificial Intelligence in Radiotherapy through Explainability
Real-Time Explainable Plausibility Verification for DNN-based Automotive Perception
XentricAI: A Gesture Sensing Calibration Approach Through Explainable and User-Centric AI
Seeking Interpretability and Explainability in Binary Activated Neural Networks
NoNE Found: Explaining the Output of Sequence-to-Sequence Models When No Named Entity Is Recognized
Influenciæ: A Library for Tracing the Influence Back to the Data-Points
Mathematical Foundation of Interpretable Equivariant Surrogate Models
Latent Space Interpretation and Visualisation for Understanding the Decisions of Convolutional Variational Autoencoders Trained with EEG Topographic Maps
LUCID-GAN: Conditional Generative Models to Locate Unfairness
Multi-Level Explainability in Radiomic-based Classification of Multiple Sclerosis and Ischemic Lesions
Mind the XAI Gap: A Human-Centered LLM Framework for Democratizing Explainable AI
CNN-Based Explanation Ensembling for Dataset, Representation and Explanations Evaluation
Interpretable World Model Imaginations as Deep Reinforcement Learning Explanation
Exploring Commonalities in Explanation Frameworks: A Multi-Domain Survey Analysis
Model Guidance via Explanations Turns Image Classifiers into Segmentation Models
Observation-Specific Explanations Through Scattered Data Approximation
What's Wrong with Your Synthetic Tabular Data? Using Explainable AI to Evaluate Generative Models
Building Personalised XAI Experiences Through iSee: a Case-Based Reasoning-Driven Platform
Statutory Professions in AI Governance and Their Consequences for Explainable AI
On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNs
Explainable Artificial Intelligence - Third World Conference, xAI 2025, Istanbul, Turkey, July 9-11, 2025, Proceedings, Part V
Cyber Risk Management with Time Varying Artificial Intelligence Models
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
Improving Local Fidelity of LIME by CVAE
Concept Extraction for Time Series with ECLAD-ts
Optimizing Deep Q-Learning Experience Replay with SHAP Explanations: Exploring Minimum Experience Replay Buffer Sizes in Reinforcement Learning
Is the Common Approach used to Identify Social Biases in Artificial Intelligence also Biased?
Explaining Vision GNNs: A Semantic and Visual Analysis of Graph-Based Image Classification
Non-experts' Trust in XAI is Unreasonably High
Towards Non-adversarial Algorithmic Recourse
V-CEM: Bridging Performance and Intervenability in Concept-Based Models
XAI-driven Model Improvements in Interpretable Image Segmentation
From Text to Space: Mapping Abstract Spatial Models in LLMs During a Grid-World Navigation Task
Explainable Deep Reinforcement Learning through Introspective Explanations
Locally Testing Model Detections for Semantic Global Concepts
Explainability Engineering Challenges: Connecting Explainability Levels to Run-Time Explainability
Xpression: A Unifying Metric to Optimize Compression and Explainability Robustness of AI Models
Differential Privacy for Anomaly Detection: Analyzing the Trade-Off Between Privacy and Explainability
Human Emotions in AI Explanations
Fairness Auditing, Explanation and Debiasing in Linguistic Data and Language Models
A Novel Model-Agnostic xAI Method Guided by Cost-Sensitive Tree Models and Argumentative Decision Graphs