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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 461470 of 971 papers

TitleStatusHype
Model-contrastive explanations through symbolic reasoningCode1
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological AttributesCode1
Evolutionary approaches to explainable machine learning0
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importanceCode0
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted?0
Benchmark data to study the influence of pre-training on explanation performance in MR image classification0
Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstration0
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
Towards Interpretability in Audio and Visual Affective Machine Learning: A Review0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
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