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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 571580 of 971 papers

TitleStatusHype
Explainable Artificial Intelligence: a Systematic Review0
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective0
Explainable Artificial Intelligence and Causal Inference based ATM Fraud Detection0
Explainable Artificial Intelligence and Cybersecurity: A Systematic Literature Review0
Explainable Artificial Intelligence and its potential within Industry0
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction0
Explainable Artificial Intelligence Based Fault Diagnosis and Insight Harvesting for Steel Plates Manufacturing0
Explainable Artificial Intelligence driven mask design for self-supervised seismic denoising0
Explainable Artificial Intelligence for Process Mining: A General Overview and Application of a Novel Local Explanation Approach for Predictive Process Monitoring0
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis0
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