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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 291300 of 971 papers

TitleStatusHype
Explainability in Music Recommender SystemsCode0
Bounded logit attention: Learning to explain image classifiersCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
Diverse Explanations From Data-Driven and Domain-Driven Perspectives in the Physical SciencesCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceCode0
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example GenerationCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical DataCode0
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