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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
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME—0
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
The Dark Side of Explanations: Poisoning Recommender Systems with Counterfactual Examples—0
Evaluating the Stability of Semantic Concept Representations in CNNs for Robust Explainability—0
Disagreement amongst counterfactual explanations: How transparency can be deceptive—0
SketchXAI: A First Look at Explainability for Human Sketches—0
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study—0
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task—0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?—0
Characterizing the contribution of dependent features in XAI methodsCode0
A Brief Review of Explainable Artificial Intelligence in Healthcare—0
Why is plausibility surprisingly problematic as an XAI criterion?—0
Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence—0
Model-agnostic explainable artificial intelligence for object detection in image dataCode0
Distrust in (X)AI -- Measurement Artifact or Distinct Construct?—0
A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics—0
Explainable Artificial Intelligence Architecture for Melanoma Diagnosis Using Indicator Localization and Self-Supervised Learning—0
Shapley-based Explainable AI for Clustering Applications in Fault Diagnosis and PrognosisCode0
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep ModelsCode1
Rough Randomness and its Application—0
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma—0
cito: An R package for training neural networks using torchCode0
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