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 121130 of 971 papers

TitleStatusHype
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
An Accelerator for Rule Induction in Fuzzy Rough TheoryCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
FreqRISE: Explaining time series using frequency maskingCode0
Ensemble of Counterfactual ExplainersCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
Evaluating saliency methods on artificial data with different background typesCode0
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
Show:102550
← PrevPage 13 of 98Next →

No leaderboard results yet.