SOTAVerified

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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 551–575 of 971 papers

TitleStatusHype
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification—0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME—0
Metric Tools for Sensitivity Analysis with Applications to Neural Networks—0
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
Impact Of Explainable AI On Cognitive Load: Insights From An Empirical Study—0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task—0
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
Model-agnostic explainable artificial intelligence for object detection in image dataCode0
Why is plausibility surprisingly problematic as an XAI criterion?—0
Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence—0
A New Deep Learning and XAI-Based Algorithm for Features Selection in Genomics—0
Distrust in (X)AI -- Measurement Artifact or Distinct Construct?—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
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
Contextual Trust—0
Show:102550
← PrevPage 23 of 39Next →

No leaderboard results yet.