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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 131140 of 971 papers

TitleStatusHype
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment0
A Complete Characterisation of ReLU-Invariant Distributions0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models0
A Survey of Explainable Knowledge Tracing0
A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting0
A Multi-Modal Explainability Approach for Human-Aware Robots in Multi-Party Conversation0
A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering0
A Survey of Accessible Explainable Artificial Intelligence Research0
A Brief Review of Explainable Artificial Intelligence in Healthcare0
Asset Pricing and Deep Learning0
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