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 241250 of 971 papers

TitleStatusHype
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
An Experimental Investigation into the Evaluation of Explainability MethodsCode0
Relevant Irrelevance: Generating Alterfactual Explanations for Image ClassifiersCode0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Acquiring Qualitative Explainable Graphs for Automated Driving Scene InterpretationCode0
Explainability in Music Recommender SystemsCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
Show:102550
← PrevPage 25 of 98Next →

No leaderboard results yet.