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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 161170 of 971 papers

TitleStatusHype
Enabling Verification of Deep Neural Networks in Perception Tasks Using Fuzzy Logic and Concept Embeddings0
Are Linear Regression Models White Box and Interpretable?0
Are Large Language Models the New Interface for Data Pipelines?0
Alterfactual Explanations -- The Relevance of Irrelevance for Explaining AI Systems0
Challenges for cognitive decoding using deep learning methods0
A Psychological Theory of Explainability0
Challenges and Opportunities in Text Generation Explainability0
A Lightweight IDS for Early APT Detection Using a Novel Feature Selection Method0
A Data-Driven Exploration of Elevation Cues in HRTFs: An Explainable AI Perspective Across Multiple Datasets0
Concept-Based Explainable Artificial Intelligence: Metrics and Benchmarks0
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