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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 251–275 of 971 papers

TitleStatusHype
Interpreting Outliers in Time Series Data through Decoding Autoencoder—0
Stacked ensemble\-based mutagenicity prediction model using multiple modalities with graph attention network—0
Breaking Down Financial News Impact: A Novel AI Approach with Geometric Hypergraphs—0
Towards Symbolic XAI -- Explanation Through Human Understandable Logical Relationships Between Features—0
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction—0
IBO: Inpainting-Based Occlusion to Enhance Explainable Artificial Intelligence Evaluation in HistopathologyCode0
Interactive dense pixel visualizations for time series and model attribution explanations—0
Underwater SONAR Image Classification and Analysis using LIME-based Explainable Artificial IntelligenceCode0
VALE: A Multimodal Visual and Language Explanation Framework for Image Classifiers using eXplainable AI and Language ModelsCode0
Dataset | Mindset = Explainable AI | Interpretable AI—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
Explainable Deep Learning Framework for Human Activity Recognition—0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
Adversarial Attack for Explanation Robustness of Rationalization Models—0
Case-based Explainability for Random Forest: Prototypes, Critics, Counter-factuals and Semi-factuals—0
Measuring User Understanding in Dialogue-based XAI Systems—0
Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing—0
Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes—0
SCENE: Evaluating Explainable AI Techniques Using Soft Counterfactuals—0
The Literature Review Network: An Explainable Artificial Intelligence for Systematic Literature Reviews, Meta-analyses, and Method Development—0
Derivation of Back-propagation for Graph Convolutional Networks using Matrix Calculus and its Application to Explainable Artificial IntelligenceCode0
A deep learning-enabled smart garment for accurate and versatile sleep conditions monitoring in daily life—0
Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis—0
Space-scale Exploration of the Poor Reliability of Deep Learning Models: the Case of the Remote Sensing of Rooftop Photovoltaic SystemsCode0
Revisiting the robustness of post-hoc interpretability methods—0
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