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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 441450 of 971 papers

TitleStatusHype
Forms of Understanding of XAI-Explanations0
The Disagreement Problem in Faithfulness Metrics0
A Hypothesis on Good Practices for AI-based Systems for Financial Time Series Forecasting: Towards Domain-Driven XAI Methods0
Explainable artificial intelligence for Healthcare applications using Random Forest Classifier with LIME and SHAP0
Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities0
Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
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
Learning impartial policies for sequential counterfactual explanations using Deep Reinforcement Learning0
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