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 461470 of 971 papers

TitleStatusHype
Refutation of Shapley Values for XAI -- Additional Evidence0
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence0
Explainable Artificial Intelligence for Drug Discovery and Development -- A Comprehensive Survey0
From Classification to Segmentation with Explainable AI: A Study on Crack Detection and Growth Monitoring0
Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence0
The role of causality in explainable artificial intelligence0
Evaluation of Human-Understandability of Global Model Explanations using Decision Tree0
On the Injunction of XAIxArt0
A Co-design Study for Multi-Stakeholder Job Recommender System ExplanationsCode0
Beyond XAI:Obstacles Towards Responsible AI0
Show:102550
← PrevPage 47 of 98Next →

No leaderboard results yet.