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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 441450 of 971 papers

TitleStatusHype
Explainable Multi-Label Classification of MBTI Types0
Explainable Multimodal Sentiment Analysis on Bengali Memes0
Explainable Predictive Maintenance0
Explainable Reinforcement Learning: A Survey0
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey0
Explainable Reinforcement Learning on Financial Stock Trading using SHAP0
Explaining machine learning models for age classification in human gait analysis0
A general approach to compute the relevance of middle-level input features0
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments0
Explaining the Deep Natural Language Processing by Mining Textual Interpretable Features0
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