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 491500 of 971 papers

TitleStatusHype
Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective0
OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture0
OMENN: One Matrix to Explain Neural Networks0
Towards trustable SHAP scores0
On Evaluating Explainability Algorithms0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations0
On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)0
On the Injunction of XAIxArt0
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach0
Show:102550
← PrevPage 50 of 98Next →

No leaderboard results yet.