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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 201225 of 971 papers

TitleStatusHype
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?Code0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
An Accelerator for Rule Induction in Fuzzy Rough TheoryCode0
Do Not Trust Additive ExplanationsCode0
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Explainability in Music Recommender SystemsCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNNCode0
Evaluating saliency methods on artificial data with different background typesCode0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
Explainability of Machine Learning Models under Missing DataCode0
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in VideoCode0
A novel approach to generate datasets with XAI ground truth to evaluate image modelsCode0
Locally-Minimal Probabilistic ExplanationsCode0
Bounded logit attention: Learning to explain image classifiersCode0
Ensemble of Counterfactual ExplainersCode0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
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