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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

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Showing 801–825 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
SoK: Explainable Machine Learning for Computer Security ApplicationsCode0
Towards Best Practice in Explaining Neural Network Decisions with LRPCode0
On Formal Feature Attribution and Its ApproximationCode0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
On the Black-box Explainability of Object Detection Models for Safe and Trustworthy Industrial ApplicationsCode0
How Well do Feature Visualizations Support Causal Understanding of CNN Activations?Code0
XAI-Based Detection of Adversarial Attacks on Deepfake DetectorsCode0
SoPa: Bridging CNNs, RNNs, and Weighted Finite-State MachinesCode0
Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic SystemsCode0
Human-in-the-loop model explanation via verbatim boundary identification in generated neighborhoodsCode0
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Do Not Trust Additive ExplanationsCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
On the Explanation of Machine Learning Predictions in Clinical Gait AnalysisCode0
An Ontology-Enabled Approach For User-Centered and Knowledge-Enabled Explanations of AI SystemsCode0
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