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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 521530 of 971 papers

TitleStatusHype
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence0
Refutation of Shapley Values for XAI -- Additional Evidence0
Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence0
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task0
Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance0
Representation, Justification and Explanation in a Value Driven Agent: An Argumentation-Based Approach0
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification0
Research on Older Adults' Interaction with E-Health Interface Based on Explainable Artificial Intelligence0
Resisting Out-of-Distribution Data Problem in Perturbation of XAI0
Responsibility: An Example-based Explainable AI approach via Training Process Inspection0
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