SOTAVerified

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 881890 of 971 papers

TitleStatusHype
Argumentation Theoretical Frameworks for Explainable Artificial Intelligence0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
An Experimentation Platform for Explainable Coalition Situational Understanding0
Interpreting convolutional networks trained on textual data0
Squashing activation functions in benchmark tests: towards eXplainable Artificial Intelligence using continuous-valued logic0
A general approach to compute the relevance of middle-level input features0
A Series of Unfortunate Counterfactual Events: the Role of Time in Counterfactual Explanations0
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction0
Explainability via Responsibility0
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis0
Show:102550
← PrevPage 89 of 98Next →

No leaderboard results yet.