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

Showing 125 of 971 papers

TitleStatusHype
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
shapiq: Shapley Interactions for Machine LearningCode4
Xplique: A Deep Learning Explainability ToolboxCode2
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and ModelsCode2
Adversarial attacks and defenses in explainable artificial intelligence: A surveyCode2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Explainable AI in Spatial AnalysisCode2
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
Driving Behavior Explanation with Multi-level FusionCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Counterfactual Shapley Additive ExplanationsCode1
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopesCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
A Fresh Look at Sanity Checks for Saliency MapsCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
Calibrated Explanations for RegressionCode1
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
An Ensemble Framework for Explainable Geospatial Machine Learning ModelsCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
Axiomatic Attribution for Deep NetworksCode1
Show:102550
← PrevPage 1 of 39Next →

No leaderboard results yet.