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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 701710 of 971 papers

TitleStatusHype
HaT5: Hate Language Identification using Text-to-Text Transfer Transformer0
Helpful, Misleading or Confusing: How Humans Perceive Fundamental Building Blocks of Artificial Intelligence Explanations0
Hierarchical Variational Autoencoder for Visual Counterfactuals0
Towards Explainable Neural-Symbolic Visual Reasoning0
How a minimal learning agent can infer the existence of unobserved variables in a complex environment0
How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network0
How Human-Centered Explainable AI Interface Are Designed and Evaluated: A Systematic Survey0
How much informative is your XAI? A decision-making assessment task to objectively measure the goodness of explanations0
How Reliable and Stable are Explanations of XAI Methods?0
How should AI decisions be explained? Requirements for Explanations from the Perspective of European Law0
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