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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 351360 of 971 papers

TitleStatusHype
Explainable-AI powered stock price prediction using time series transformers: A Case Study on BIST1000
Explainable AI through the Learning of Arguments0
Explainable AI via Learning to Optimize0
Explainable Analysis of Deep Learning Methods for SAR Image Classification0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis0
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework0
Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models0
Explainable Artificial Intelligence Recommendation System by Leveraging the Semantics of Adverse Childhood Experiences: Proof-of-Concept Prototype Development0
Explainable Artificial Intelligence Approaches: A Survey0
Explainable Artificial Intelligence for Assault Sentence Prediction in New Zealand0
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