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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 501–525 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
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