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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
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Consistent Explanations by Contrastive LearningCode1
Counterfactual Shapley Additive ExplanationsCode1
Driving Behavior Explanation with Multi-level FusionCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation TestCode1
A Fresh Look at Sanity Checks for Saliency MapsCode1
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopesCode1
survex: an R package for explaining machine learning survival modelsCode1
TE2Rules: Explaining Tree Ensembles using RulesCode1
Axiomatic Attribution for Deep NetworksCode1
The Grammar of Interactive Explanatory Model AnalysisCode1
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
Extracting human interpretable structure-property relationships in chemistry using XAI and large language modelsCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
Explainable Earth Surface Forecasting under Extreme EventsCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Logic Explained NetworksCode1
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