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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 51–100 of 971 papers

TitleStatusHype
ProtoPFormer: Concentrating on Prototypical Parts in Vision Transformers for Interpretable Image RecognitionCode1
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
TE2Rules: Explaining Tree Ensembles using RulesCode1
Towards ML Methods for Biodiversity: A Novel Wild Bee Dataset and Evaluations of XAI Methods for ML-Assisted Rare Species AnnotationsCode1
From Attribution Maps to Human-Understandable Explanations through Concept Relevance PropagationCode1
Towards Better Understanding Attribution MethodsCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
NLX-GPT: A Model for Natural Language Explanations in Vision and Vision-Language TasksCode1
XAutoML: A Visual Analytics Tool for Understanding and Validating Automated Machine LearningCode1
Guidelines and Evaluation of Clinical Explainable AI in Medical Image AnalysisCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Evaluation of Interpretability for Deep Learning algorithms in EEG Emotion Recognition: A case study in AutismCode1
Counterfactual Shapley Additive ExplanationsCode1
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanationsCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Consistent Explanations by Contrastive LearningCode1
Focus! Rating XAI Methods and Finding BiasesCode1
Logic Explained NetworksCode1
Toward Improving Confidence in Autonomous Vehicle Software: A Study on Traffic Sign Recognition SystemsCode1
Quantitative Evaluation of Explainable Graph Neural Networks for Molecular Property PredictionCode1
Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAyCode1
Entropy-based Logic Explanations of Neural NetworksCode1
To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodsCode1
This Looks Like That... Does it? Shortcomings of Latent Space Prototype Interpretability in Deep NetworksCode1
Visualizing Adapted Knowledge in Domain TransferCode1
Text Guide: Improving the quality of long text classification by a text selection method based on feature importanceCode1
Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark DatasetCode1
TorchPRISM: Principal Image Sections Mapping, a novel method for Convolutional Neural Network features visualizationCode1
Driving Behavior Explanation with Multi-level FusionCode1
Landscape of R packages for eXplainable Artificial IntelligenceCode1
SCOUTER: Slot Attention-based Classifier for Explainable Image RecognitionCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
The Grammar of Interactive Explanatory Model AnalysisCode1
Learning to Structure an Image with Few ColorsCode1
Proposed Guidelines for the Responsible Use of Explainable Machine LearningCode1
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple BenchmarkCode1
Axiomatic Attribution for Deep NetworksCode1
Explainable Artificial Intelligence in Biomedical Image Analysis: A Comprehensive Survey—0
From Motion to Meaning: Biomechanics-Informed Neural Network for Explainable Cardiovascular Disease Identification—0
Towards Transparent AI: A Survey on Explainable Large Language Models—0
IXAII: An Interactive Explainable Artificial Intelligence Interface for Decision Support Systems—0
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach—0
Toward the Explainability of Protein Language Models for Sequence Design—0
When concept-based XAI is imprecise: Do people distinguish between generalisations and misrepresentations?—0
A Systematic Review of User-Centred Evaluation of Explainable AI in Healthcare—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
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