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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 8190 of 971 papers

TitleStatusHype
Learning to Structure an Image with Few ColorsCode1
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
MEGAN: Multi-Explanation Graph Attention NetworkCode1
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple BenchmarkCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
Counterfactual Shapley Additive ExplanationsCode1
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAyCode1
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