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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 781790 of 971 papers

TitleStatusHype
CAManim: Animating end-to-end network activation maps0
Can Explainable AI Explain Unfairness? A Framework for Evaluating Explainable AI0
Can Requirements Engineering Support Explainable Artificial Intelligence? Towards a User-Centric Approach for Explainability Requirements0
Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals0
CAT: Concept-level backdoor ATtacks for Concept Bottleneck Models0
Causal Explanations and XAI0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning0
Challenges and Opportunities in Text Generation Explainability0
Challenges for cognitive decoding using deep learning methods0
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