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Explainable artificial intelligence

XAI refers to methods and techniques in the application of artificial intelligence (AI) such that the results of the solution can be understood by humans. It contrasts with the concept of the "black box" in machine learning where even its designers cannot explain why an AI arrived at a specific decision. XAI may be an implementation of the social right to explanation. XAI is relevant even if there is no legal right or regulatory requirement—for example, XAI can improve the user experience of a product or service by helping end users trust that the AI is making good decisions. This way the aim of XAI is to explain what has been done, what is done right now, what will be done next and unveil the information the actions are based on. These characteristics make it possible (i) to confirm existing knowledge (ii) to challenge existing knowledge and (iii) to generate new assumptions.

Papers

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Showing 501–550 of 971 papers

TitleStatusHype
What's meant by explainable model: A Scoping Review—0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
Visual Explanations with Attributions and Counterfactuals on Time Series Classification—0
Gastrointestinal Disease Classification through Explainable and Cost-Sensitive Deep Neural Networks with Supervised Contrastive LearningCode0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising—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
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
Impact of Feature Encoding on Malware Classification Explainability—0
A Novel Explainable Artificial Intelligence Model in Image Classification problem—0
On Formal Feature Attribution and Its ApproximationCode0
Human in the AI loop via xAI and Active Learning for Visual Inspection—0
The future of human-centric eXplainable Artificial Intelligence (XAI) is not post-hoc explanations—0
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification ProblemsCode0
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
Delivering Inflated ExplanationsCode0
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
Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration—0
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review—0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
For Better or Worse: The Impact of Counterfactual Explanations' Directionality on User Behavior in xAICode0
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions—0
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital MedicineCode0
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
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making—0
Scalable Concept Extraction in Industry 4.0—0
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
From Robustness to Explainability and Back Again—0
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility—0
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables—0
Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples—0
Counterfactual Formulation of Patient-Specific Root Causes of Disease—0
An Experimental Investigation into the Evaluation of Explainability MethodsCode0
PIC-XAI: Post-hoc Image Captioning Explanation using SegmentationCode0
A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering—0
Unveiling the Potential of Counterfactuals Explanations in Employability—0
Disproving XAI Myths with Formal Methods -- Initial Results—0
eXplainable Artificial Intelligence on Medical Images: A Survey—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises—0
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models—0
Hardware Acceleration of Explainable Artificial Intelligence—0
Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI—0
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