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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 801–850 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
Visual explanations for polyp detection: How medical doctors assess intrinsic versus extrinsic explanationsCode0
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model ConversationsCode0
Towards explainable artificial intelligence (XAI) for early anticipation of traffic accidentsCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
Visual Interpretability for Deep Learning: a SurveyCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Impact of lung segmentation on the diagnosis and explanation of COVID-19 in chest X-ray imagesCode0
Impact of satellites streaks for observational astronomy: a study on data captured during one year from Luxembourg Greater RegionCode0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
PIC-XAI: Post-hoc Image Captioning Explanation using SegmentationCode0
PixleepFlow: A Pixel-Based Lifelog Framework for Predicting Sleep Quality and Stress LevelCode0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Towards Explainable Multimodal Depression Recognition for Clinical InterviewsCode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Study on the Helpfulness of Explainable Artificial IntelligenceCode0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 DetectionCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial IntelligenceCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Predicting and Understanding Human Action Decisions during Skillful Joint-Action via Machine Learning and Explainable-AICode0
Decoding the Protein-ligand Interactions Using Parallel Graph Neural NetworksCode0
Unified Explanations in Machine Learning Models: A Perturbation ApproachCode0
An Accelerator for Rule Induction in Fuzzy Rough TheoryCode0
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