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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 351360 of 971 papers

TitleStatusHype
Explainable Multimodal Sentiment Analysis on Bengali Memes0
Concept-based Explainable Artificial Intelligence: A Survey0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
CAManim: Animating end-to-end network activation maps0
Locally-Minimal Probabilistic ExplanationsCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations0
Anytime Approximate Formal Feature Attribution0
Explain To Decide: A Human-Centric Review on the Role of Explainable Artificial Intelligence in AI-assisted Decision Making0
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