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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 941950 of 971 papers

TitleStatusHype
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studiesCode0
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNNCode0
Does Dataset Complexity Matters for Model Explainers?Code0
This looks like what? Challenges and Future Research Directions for Part-Prototype ModelsCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
Meaningful Data Sampling for a Faithful Local Explanation MethodCode0
Rule-based Out-Of-Distribution DetectionCode0
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