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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 951960 of 971 papers

TitleStatusHype
A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI0
Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applications0
Proposed Guidelines for the Responsible Use of Explainable Machine LearningCode1
Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction0
Do Not Trust Additive ExplanationsCode0
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
A Grounded Interaction Protocol for Explainable Artificial Intelligence0
Explainable Artificial Intelligence and its potential within Industry0
Some Insights Towards a Unified Semantic Representation of Explanation for eXplainable Artificial Intelligence0
Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)0
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