SOTAVerified

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 926–950 of 971 papers

TitleStatusHype
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
Using Explainable AI and Transfer Learning to understand and predict the maintenance of Atlantic blocking with limited observational dataCode0
Looking into Concept Explanation Methods for Diabetic Retinopathy ClassificationCode0
Explanations for Answer Set ProgrammingCode0
Explanations of Black-Box Model Predictions by Contextual Importance and UtilityCode0
Causal Discovery and Classification Using Lempel-Ziv ComplexityCode0
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood GenerationCode0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical DataCode0
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental AnalysisCode0
Exploring deterministic frequency deviations with explainable AICode0
A comprehensive study on fidelity metrics for XAICode0
XAI in the context of Predictive Process Monitoring: Too much to RevealCode0
Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentationsCode0
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studiesCode0
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNNCode0
Does Dataset Complexity Matters for Model Explainers?Code0
This looks like what? Challenges and Future Research Directions for Part-Prototype ModelsCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
Meaningful Data Sampling for a Faithful Local Explanation MethodCode0
Rule-based Out-Of-Distribution DetectionCode0
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