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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

Showing 5175 of 971 papers

TitleStatusHype
MEGAN: Multi-Explanation Graph Attention NetworkCode1
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
A Fresh Look at Sanity Checks for Saliency MapsCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep ModelsCode1
Calibrated Explanations for RegressionCode1
SCOUTER: Slot Attention-based Classifier for Explainable Image RecognitionCode1
survex: an R package for explaining machine learning survival modelsCode1
TE2Rules: Explaining Tree Ensembles using RulesCode1
Counterfactual Shapley Additive ExplanationsCode1
The Grammar of Interactive Explanatory Model AnalysisCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
Axiomatic Attribution for Deep NetworksCode1
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopesCode1
Driving Behavior Explanation with Multi-level FusionCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Entropy-based Logic Explanations of Neural NetworksCode1
Landscape of R packages for eXplainable Artificial IntelligenceCode1
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