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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 91100 of 971 papers

TitleStatusHype
Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic0
Found in Translation: semantic approaches for enhancing AI interpretability in face verification0
Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text0
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity0
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example GenerationCode0
Extending XReason: Formal Explanations for Adversarial Detection0
The Role of XAI in Transforming Aeronautics and Aerospace Systems0
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models0
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