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Explainable Artificial Intelligence (XAI)

Explainable Artificial Intelligence

Papers

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Showing 351–400 of 1041 papers

TitleStatusHype
Visual Explanations with Attributions and Counterfactuals on Time Series Classification—0
Is Task-Agnostic Explainable AI a Myth?—0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations—0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
Impact of Feature Encoding on Malware Classification Explainability—0
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local ExplanationsCode0
On Formal Feature Attribution and Its ApproximationCode0
Interpretable and Secure Trajectory Optimization for UAV-Assisted Communication—0
Human in the AI loop via xAI and Active Learning for Visual Inspection—0
Fixing confirmation bias in feature attribution methods via semantic match—0
Towards Explainable AI for Channel Estimation in Wireless Communications—0
CLIMAX: An exploration of Classifier-Based Contrastive ExplanationsCode0
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations—0
Explainability is NOT a Game—0
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods—0
xAI-CycleGAN, a Cycle-Consistent Generative Assistive Network—0
Delivering Inflated ExplanationsCode0
PWSHAP: A Path-Wise Explanation Model for Targeted VariablesCode0
Manipulation Risks in Explainable AI: The Implications of the Disagreement Problem—0
Are Good Explainers Secretly Human-in-the-Loop Active Learners?—0
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological AttributesCode1
Evolutionary approaches to explainable machine learning—0
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importanceCode0
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?—0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification—0
Detection of Sensor-To-Sensor Variations using Explainable AI—0
Process Knowledge-infused Learning for Clinician-friendly Explanations—0
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review—0
Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability—0
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAICode0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
Active Globally Explainable Learning for Medical Images via Class Association Embedding and Cyclic Adversarial Generation—0
Strategies to exploit XAI to improve classification systems—0
Explainable Predictive Maintenance—0
IsoEx: an explainable unsupervised approach to process event logs cyber investigation—0
Dear XAI Community, We Need to Talk! Fundamental Misconceptions in Current XAI Research—0
Adversarial attacks and defenses in explainable artificial intelligence: A surveyCode2
Explainable AI using expressive Boolean formulas—0
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
Scalable Concept Extraction in Industry 4.0—0
Explaining AI in Finance: Past, Present, ProspectsCode0
From Robustness to Explainability and Back Again—0
XAI Renaissance: Redefining Interpretability in Medical Diagnostic Models—0
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables—0
Rethinking Model Evaluation as Narrowing the Socio-Technical Gap—0
Explainable AI for Malnutrition Risk Prediction from m-Health and Clinical Data—0
Can We Trust Explainable AI Methods on ASR? An Evaluation on Phoneme Recognition—0
Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples—0
Explaining Deep Learning for ECG Analysis: Building Blocks for Auditing and Knowledge DiscoveryCode0
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