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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 1–50 of 971 papers

TitleStatusHype
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraintsCode5
shapiq: Shapley Interactions for Machine LearningCode4
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Explainable AI in Spatial AnalysisCode2
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and ModelsCode2
Adversarial attacks and defenses in explainable artificial intelligence: A surveyCode2
Xplique: A Deep Learning Explainability ToolboxCode2
Landscape of R packages for eXplainable Artificial IntelligenceCode1
GNNExplainer: Generating Explanations for Graph Neural NetworksCode1
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception TasksCode1
From Attribution Maps to Human-Understandable Explanations through Concept Relevance PropagationCode1
Gaussian Process Regression With Interpretable Sample-Wise Feature WeightsCode1
Proposed Guidelines for the Responsible Use of Explainable Machine LearningCode1
AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple BenchmarkCode1
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
Explainable Earth Surface Forecasting under Extreme EventsCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Explainable AI Components for Narrative Map ExtractionCode1
Explainable Deep Learning Methods in Medical Image Classification: A SurveyCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Explaining deep learning models for spoofing and deepfake detection with SHapley Additive exPlanationsCode1
Finding Alignments Between Interpretable Causal Variables and Distributed Neural RepresentationsCode1
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI MethodsCode1
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopesCode1
Entropy-based Logic Explanations of Neural NetworksCode1
Guidelines and Evaluation of Clinical Explainable AI in Medical Image AnalysisCode1
Insights Into the Inner Workings of Transformer Models for Protein Function PredictionCode1
An Ensemble Framework for Explainable Geospatial Machine Learning ModelsCode1
Consistent Explanations by Contrastive LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
A Fresh Look at Sanity Checks for Saliency MapsCode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Counterfactual Shapley Additive ExplanationsCode1
Driving Behavior Explanation with Multi-level FusionCode1
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
A Song of (Dis)agreement: Evaluating the Evaluation of Explainable Artificial Intelligence in Natural Language ProcessingCode1
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
Explainable AI for Bioinformatics: Methods, Tools, and ApplicationsCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Axiomatic Attribution for Deep NetworksCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
BASED-XAI: Breaking Ablation Studies Down for Explainable Artificial IntelligenceCode1
Explaining Black-Box Models through CounterfactualsCode1
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
Calibrated Explanations for RegressionCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
ContrXT: Generating Contrastive Explanations from any Text ClassifierCode1
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