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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 301–325 of 971 papers

TitleStatusHype
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in ExplanationsCode0
Generative and Explainable AI for High-Dimensional Channel EstimationCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
Rational Shapley ValuesCode0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence—0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation—0
Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators—0
Disproving XAI Myths with Formal Methods -- Initial Results—0
Automatic Diagnosis of Myocarditis Disease in Cardiac MRI Modality using Deep Transformers and Explainable Artificial Intelligence—0
An Explainable AI Framework for Artificial Intelligence of Medical Things—0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering—0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification—0
Disagreement amongst counterfactual explanations: How transparency can be deceptive—0
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
An Experimentation Platform for Explainable Coalition Situational Understanding—0
Directions for Explainable Knowledge-Enabled Systems—0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control—0
Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy—0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning—0
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning—0
Automated Explanation Selection for Scientific Discovery—0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence—0
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data—0
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