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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 611620 of 971 papers

TitleStatusHype
OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture0
Greybox XAI: a Neural-Symbolic learning framework to produce interpretable predictions for image classificationCode0
Asset Pricing and Deep Learning0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Survey on Deep Fuzzy Systems in regression applications: a view on interpretability0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection0
Regulating eXplainable Artificial Intelligence (XAI) May Harm Consumers0
Explainable Artificial Intelligence to Detect Image Spam Using Convolutional Neural Network0
Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams0
Generating detailed saliency maps using model-agnostic methods0
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