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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 171180 of 971 papers

TitleStatusHype
Characterizing the contribution of dependent features in XAI methodsCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
cito: An R package for training neural networks using torchCode0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical DataCode0
A Co-design Study for Multi-Stakeholder Job Recommender System ExplanationsCode0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
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