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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 721730 of 971 papers

TitleStatusHype
Anytime Approximate Formal Feature Attribution0
A Peek Into the Reasoning of Neural Networks: Interpreting with Structural Visual Concepts0
Applications of Explainable artificial intelligence in Earth system science0
Applying XAI based unsupervised knowledge discovering for Operation modes in a WWTP. A real case: AQUAVALL WWTP0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing0
Approximating the Shapley Value without Marginal Contributions0
A Practical guide on Explainable AI Techniques applied on Biomedical use case applications0
A Psychological Theory of Explainability0
Are Large Language Models the New Interface for Data Pipelines?0
Are Linear Regression Models White Box and Interpretable?0
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