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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 951960 of 971 papers

TitleStatusHype
Few-Shot Multimodal Explanation for Visual Question AnsweringCode0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
Doctor XAvIer: Explainable Diagnosis on Physician-Patient Dialogues and XAI EvaluationCode0
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceCode0
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example GenerationCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
CohEx: A Generalized Framework for Cohort ExplanationCode0
Coherent Local Explanations for Mathematical OptimizationCode0
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