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 601610 of 971 papers

TitleStatusHype
Towards Robust Ferrous Scrap Material Classification with Deep Learning and Conformal Prediction0
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features0
Towards the Linear Algebra Based Taxonomy of XAI Explanations0
Towards the Visualization of Aggregated Class Activation Maps to Analyse the Global Contribution of Class Features0
Towards Transparent AI: A Survey on Explainable Large Language Models0
Towards Transparent and Accurate Diabetes Prediction Using Machine Learning and Explainable Artificial Intelligence0
Towards Understanding Human Functional Brain Development with Explainable Artificial Intelligence: Challenges and Perspectives0
Toward the application of XAI methods in EEG-based systems0
Toward the Explainability of Protein Language Models for Sequence Design0
Transcending XAI Algorithm Boundaries through End-User-Inspired Design0
Show:102550
← PrevPage 61 of 98Next →

No leaderboard results yet.