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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 851860 of 971 papers

TitleStatusHype
T5 for Hate Speech, Augmented Data and EnsembleCode0
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme HeatwavesCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree EnsemblesCode0
Procedural Fairness in Machine LearningCode0
Visualizing and Understanding Contrastive LearningCode0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AICode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
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