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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 221230 of 971 papers

TitleStatusHype
Explainability of Machine Learning Models under Missing DataCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Evaluating saliency methods on artificial data with different background typesCode0
Explainability in Music Recommender SystemsCode0
A novel approach to generate datasets with XAI ground truth to evaluate image modelsCode0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Bounded logit attention: Learning to explain image classifiersCode0
Multi-modal volumetric concept activation to explain detection and classification of metastatic prostate cancer on PSMA-PET/CTCode0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
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