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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 331340 of 971 papers

TitleStatusHype
LLMs for XAI: Future Directions for Explaining Explanations0
Relevant Irrelevance: Generating Alterfactual Explanations for Image ClassifiersCode0
Counterfactual and Semifactual Explanations in Abstract Argumentation: Formal Foundations, Complexity and Computation0
False Sense of Security in Explainable Artificial Intelligence (XAI)0
Isopignistic Canonical Decomposition via Belief Evolution Network0
Explainable Interface for Human-Autonomy Teaming: A Survey0
Explainable Multi-Label Classification of MBTI Types0
An Explainable and Conformal AI Model to Detect Temporomandibular Joint Involvement in Children Suffering from Juvenile Idiopathic Arthritis0
Towards trustable SHAP scores0
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle0
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