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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 101110 of 971 papers

TitleStatusHype
ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.00
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
Meta-evaluating stability measures: MAX-Senstivity & AVG-SensitivityCode0
Assessing high-order effects in feature importance via predictability decomposition0
Discrete Subgraph Sampling for Interpretable Graph based Visual Question AnsweringCode0
REPEAT: Improving Uncertainty Estimation in Representation Learning ExplainabilityCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
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