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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 381390 of 971 papers

TitleStatusHype
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
Transferability and explainability of deep learning emulators for regional climate model projections: Perspectives for future applications0
Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective0
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning0
Medical Image Denosing via Explainable AI Feature Preserving Loss0
Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions0
Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques0
Using Slisemap to interpret physical dataCode1
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
Reputation-Based Federated Learning Defense to Mitigate Threats in EEG Signal Classification0
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