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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 131140 of 971 papers

TitleStatusHype
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Explainable expected goal models for performance analysis in football analyticsCode0
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceCode0
Ensemble of Counterfactual ExplainersCode0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
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