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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 541550 of 971 papers

TitleStatusHype
A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering0
Unveiling the Potential of Counterfactuals Explanations in Employability0
Disproving XAI Myths with Formal Methods -- Initial Results0
eXplainable Artificial Intelligence on Medical Images: A Survey0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion0
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises0
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models0
Hardware Acceleration of Explainable Artificial Intelligence0
Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI0
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