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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 791800 of 971 papers

TitleStatusHype
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models0
ChatGPT or Human? Detect and Explain. Explaining Decisions of Machine Learning Model for Detecting Short ChatGPT-generated Text0
Clash of the Explainers: Argumentation for Context-Appropriate Explanations0
Classification and Explanation of Distributed Denial-of-Service (DDoS) Attack Detection using Machine Learning and Shapley Additive Explanation (SHAP) Methods0
Classification of Viral Pneumonia X-ray Images with the Aucmedi Framework0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos0
CNN-based explanation ensembling for dataset, representation and explanations evaluation0
A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME0
Comparative Analysis of Hyperspectral Image Reconstruction Using Deep Learning for Agricultural and Biological Applications0
Comparing interpretation methods in mental state decoding analyses with deep learning models0
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