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 351360 of 971 papers

TitleStatusHype
An Artificial Intelligence-based model for cell killing prediction: development, validation and explainability analysis of the ANAKIN model0
Adherence and Constancy in LIME-RS Explanations for Recommendation0
Abstraction, Validation, and Generalization for Explainable Artificial Intelligence0
Deciphering AutoML Ensembles: cattleia's Assistance in Decision-Making0
DCNFIS: Deep Convolutional Neuro-Fuzzy Inference System0
A Theoretical Framework for AI Models Explainability with Application in Biomedicine0
Dataset | Mindset = Explainable AI | Interpretable AI0
Data Representing Ground-Truth Explanations to Evaluate XAI Methods0
A Temporal Type-2 Fuzzy System for Time-dependent Explainable Artificial Intelligence0
An Argumentation-based Approach for Explaining Goal Selection in Intelligent Agents0
Show:102550
← PrevPage 36 of 98Next →

No leaderboard results yet.