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 201210 of 971 papers

TitleStatusHype
Contextual Trust0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning0
Convolutional Neural Network Interpretability with General Pattern Theory0
Regularizing Explanations in Bayesian Convolutional Neural Networks0
Correlation between morphological evolution of splashing drop and exerted impact force revealed by interpretation of explainable artificial intelligence0
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation0
A survey on Concept-based Approaches For Model Improvement0
Counterfactual Explanations for Clustering Models0
Counterfactual Explanations of Black-box Machine Learning Models using Causal Discovery with Applications to Credit Rating0
Deep Learning for predicting rate-induced tipping0
Show:102550
← PrevPage 21 of 98Next →

No leaderboard results yet.