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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 661670 of 971 papers

TitleStatusHype
Exploring Explainable AI in the Financial Sector: Perspectives of Banks and Supervisory Authorities0
Extending XReason: Formal Explanations for Adversarial Detection0
ExTRA: Explainable Therapy-Related Annotations0
FairLens: Auditing Black-box Clinical Decision Support Systems0
False Sense of Security in Explainable Artificial Intelligence (XAI)0
Fault Diagnosis using eXplainable AI: a Transfer Learning-based Approach for Rotating Machinery exploiting Augmented Synthetic Data0
Feature Visualization within an Automated Design Assessment leveraging Explainable Artificial Intelligence Methods0
Financial Fraud Detection Using Explainable AI and Stacking Ensemble Methods0
Finding Words Associated with DIF: Predicting Differential Item Functioning using LLMs and Explainable AI0
Fiper: a Visual-based Explanation Combining Rules and Feature Importance0
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