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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 241250 of 971 papers

TitleStatusHype
Developing Guidelines for Functionally-Grounded Evaluation of Explainable Artificial Intelligence using Tabular Data0
Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence0
Diagnosis of Paratuberculosis in Histopathological Images Based on Explainable Artificial Intelligence and Deep Learning0
DiCE-Extended: A Robust Approach to Counterfactual Explanations in Machine Learning0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control0
Directions for Explainable Knowledge-Enabled Systems0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems0
Disagreement amongst counterfactual explanations: How transparency can be deceptive0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering0
ApproXAI: Energy-Efficient Hardware Acceleration of Explainable AI using Approximate Computing0
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