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 691700 of 971 papers

TitleStatusHype
A Grounded Interaction Protocol for Explainable Artificial Intelligence0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.00
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods0
AI Approaches in Processing and Using Data in Personalized Medicine0
AI Readiness in Healthcare through Storytelling XAI0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method0
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems0
A Means-End Account of Explainable Artificial Intelligence0
A Meta-Analysis of the Utility of Explainable Artificial Intelligence in Human-AI Decision-Making0
A Meta Survey of Quality Evaluation Criteria in Explanation Methods0
Show:102550
← PrevPage 70 of 98Next →

No leaderboard results yet.