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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 551560 of 971 papers

TitleStatusHype
Explainable AI in Grassland Monitoring: Enhancing Model Performance and Domain Adaptability0
Explainable AI Integrated Feature Engineering for Wildfire Prediction0
Explainable AI is Dead, Long Live Explainable AI! Hypothesis-driven decision support0
Explainable AI meets Healthcare: A Study on Heart Disease Dataset0
Explainable AI Methods for Multi-Omics Analysis: A Survey0
Explainable AI needs formal notions of explanation correctness0
Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions0
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
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