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 341350 of 971 papers

TitleStatusHype
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in VideoCode0
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free MetricCode1
A comprehensive study on fidelity metrics for XAICode0
Eclectic Rule Extraction for Explainability of Deep Neural Network based Intrusion Detection Systems0
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation TestCode1
Towards Explainable Artificial Intelligence (XAI): A Data Mining Perspective0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis0
Manifold-based Shapley for SAR Recognization Network Explanation0
XXAI: Towards eXplicitly eXplainable Artificial Intelligence0
Show:102550
← PrevPage 35 of 98Next →

No leaderboard results yet.