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
Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis0
The design and implementation of Language Learning Chatbot with XAI using Ontology and Transfer Learning0
Landscape of R packages for eXplainable Artificial IntelligenceCode1
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors0
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing0
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray imagesCode0
Better Model Selection with a new Definition of Feature Importance0
SCOUTER: Slot Attention-based Classifier for Explainable Image RecognitionCode1
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents0
Should We Trust (X)AI? Design Dimensions for Structured Experimental Evaluations0
Show:102550
← PrevPage 89 of 98Next →

No leaderboard results yet.