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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
PnPXAI: A Universal XAI Framework Providing Automatic Explanations Across Diverse Modalities and ModelsCode2
Explainable AI in Spatial AnalysisCode2
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Adversarial attacks and defenses in explainable artificial intelligence: A surveyCode2
Xplique: A Deep Learning Explainability ToolboxCode2
TIMING: Temporality-Aware Integrated Gradients for Time Series ExplanationCode1
Explainable AI Components for Narrative Map ExtractionCode1
A Wearable Device Dataset for Mental Health Assessment Using Laser Doppler Flowmetry and Fluorescence Spectroscopy SensorsCode1
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2Code1
Explainable Earth Surface Forecasting under Extreme EventsCode1
Confident Teacher, Confident Student? A Novel User Study Design for Investigating the Didactic Potential of Explanations and their Impact on UncertaintyCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Explanation as a Watermark: Towards Harmless and Multi-bit Model Ownership Verification via Watermarking Feature AttributionCode1
A Fresh Look at Sanity Checks for Saliency MapsCode1
Automatic Extraction of Linguistic Description from Fuzzy Rule BaseCode1
Beyond Pixels: Enhancing LIME with Hierarchical Features and Segmentation Foundation ModelsCode1
An Ensemble Framework for Explainable Geospatial Machine Learning ModelsCode1
LangXAI: Integrating Large Vision Models for Generating Textual Explanations to Enhance Explainability in Visual Perception TasksCode1
Deep Learning for Gamma-Ray Bursts: A data driven event framework for X/Gamma-Ray analysis in space telescopesCode1
Word-Level ASR Quality Estimation for Efficient Corpus Sampling and Post-Editing through Analyzing Attentions of a Reference-Free MetricCode1
MICA: Towards Explainable Skin Lesion Diagnosis via Multi-Level Image-Concept AlignmentCode1
Sanity Checks Revisited: An Exploration to Repair the Model Parameter Randomisation TestCode1
Explaining black boxes with a SMILE: Statistical Model-agnostic Interpretability with Local ExplanationsCode1
Extracting human interpretable structure-property relationships in chemistry using XAI and large language modelsCode1
Using Slisemap to interpret physical dataCode1
Local Universal Explainer (LUX) -- a rule-based explainer with factual, counterfactual and visual explanationsCode1
In-Context Explainers: Harnessing LLMs for Explaining Black Box ModelsCode1
Extending CAM-based XAI methods for Remote Sensing Imagery SegmentationCode1
Trainable Noise Model as an XAI evaluation method: application on Sobol for remote sensing image segmentationCode1
Insights Into the Inner Workings of Transformer Models for Protein Function PredictionCode1
Calibrated Explanations for RegressionCode1
survex: an R package for explaining machine learning survival modelsCode1
Explaining Black-Box Models through CounterfactualsCode1
FunnyBirds: A Synthetic Vision Dataset for a Part-Based Analysis of Explainable AI MethodsCode1
Model-contrastive explanations through symbolic reasoningCode1
WBCAtt: A White Blood Cell Dataset Annotated with Detailed Morphological AttributesCode1
ExpPoint-MAE: Better interpretability and performance for self-supervised point cloud transformersCode1
Unlocking the black box of CNNs: Visualising the decision-making process with PRISMCode1
Calibrated Explanations: with Uncertainty Information and CounterfactualsCode1
An XAI framework for robust and transparent data-driven wind turbine power curve modelsCode1
Reveal to Revise: An Explainable AI Life Cycle for Iterative Bias Correction of Deep ModelsCode1
Towards Trust of Explainable AI in Thyroid Nodule DiagnosisCode1
Finding Alignments Between Interpretable Causal Variables and Distributed Neural RepresentationsCode1
Explainable AI for Bioinformatics: Methods, Tools, and ApplicationsCode1
Causality-Aware Local Interpretable Model-Agnostic ExplanationsCode1
MEGAN: Multi-Explanation Graph Attention NetworkCode1
XAI for transparent wind turbine power curve modelsCode1
Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI SolutionsCode1
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