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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 841850 of 971 papers

TitleStatusHype
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
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations0
Disagreement amongst counterfactual explanations: How transparency can be deceptive0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering0
Disproving XAI Myths with Formal Methods -- Initial Results0
Distance-Restricted Explanations: Theoretical Underpinnings & Efficient Implementation0
Distributed Linguistic Representations in Decision Making: Taxonomy, Key Elements and Applications, and Challenges in Data Science and Explainable Artificial Intelligence0
Distrust in (X)AI -- Measurement Artifact or Distinct Construct?0
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