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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 801810 of 971 papers

TitleStatusHype
Heart2Mind: Human-Centered Contestable Psychiatric Disorder Diagnosis System using Wearable ECG MonitorsCode0
NormEnsembleXAI: Unveiling the Strengths and Weaknesses of XAI Ensemble TechniquesCode0
Hierarchical Explanations for Video Action RecognitionCode0
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value ApproximationCode0
Higher-order Neural Additive Models: An Interpretable Machine Learning Model with Feature InteractionsCode0
TSEM: Temporally Weighted Spatiotemporal Explainable Neural Network for Multivariate Time SeriesCode0
HiTZ@Antidote: Argumentation-driven Explainable Artificial Intelligence for Digital MedicineCode0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
On Background Bias of Post-Hoc Concept Embeddings in Computer Vision DNNsCode0
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