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 311320 of 971 papers

TitleStatusHype
An Explainable AI Framework for Artificial Intelligence of Medical Things0
Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering0
Automated Quality Control of Vacuum Insulated Glazing by Convolutional Neural Network Image Classification0
Disagreement amongst counterfactual explanations: How transparency can be deceptive0
Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures0
An Experimentation Platform for Explainable Coalition Situational Understanding0
Directions for Explainable Knowledge-Enabled Systems0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control0
Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy0
Show:102550
← PrevPage 32 of 98Next →

No leaderboard results yet.