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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 901–950 of 971 papers

TitleStatusHype
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood GenerationCode0
timeXplain -- A Framework for Explaining the Predictions of Time Series ClassifiersCode0
Locality Guided Neural Networks for Explainable Artificial Intelligence—0
Am I Building a White Box Agent or Interpreting a Black Box Agent?—0
Drug discovery with explainable artificial intelligence—0
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels—0
Embedded Encoder-Decoder in Convolutional Networks Towards Explainable AICode1
Does Explainable Artificial Intelligence Improve Human Decision-Making?—0
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey—0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Explanations of Black-Box Model Predictions by Contextual Importance and UtilityCode0
Explainable Artificial Intelligence: a Systematic Review—0
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation—0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
Evolved Explainable Classifications for Lymph Node Metastases—0
Explainable Reinforcement Learning: A Survey—0
Post-hoc explanation of black-box classifiers using confident itemsets—0
A multi-component framework for the analysis and design of explainable artificial intelligence—0
LIMEtree: Consistent and Faithful Multi-class ExplanationsCode0
Explaining How Deep Neural Networks Forget by Deep VisualizationCode0
The Grammar of Interactive Explanatory Model AnalysisCode1
Explainable Goal-Driven Agents and Robots -- A Comprehensive Review—0
Foundations of Explainable Knowledge-Enabled Systems—0
Learning to Structure an Image with Few ColorsCode1
Directions for Explainable Knowledge-Enabled Systems—0
Vector symbolic architectures for context-free grammars—0
EXPLAIN-IT: Towards Explainable AI for Unsupervised Network Traffic Analysis—0
The Pragmatic Turn in Explainable Artificial Intelligence (XAI)—0
Towards explainable meta-learning—0
What Would You Ask the Machine Learning Model? Identification of User Needs for Model Explanations Based on Human-Model ConversationsCode0
Explainable Artificial Intelligence and Machine Learning: A reality rooted perspective—0
On the Explanation of Machine Learning Predictions in Clinical Gait AnalysisCode0
Improved Explanatory Efficacy on Human Affect and Workload through Interactive Process in Artificial Intelligence—0
Explainability Fact Sheets: A Framework for Systematic Assessment of Explainable Approaches—0
Explainable artificial intelligence model to predict acute critical illness from electronic health records—0
Towards Quantification of Explainability in Explainable Artificial Intelligence Methods—0
Domain Knowledge Aided Explainable Artificial Intelligence for Intrusion Detection and Response—0
Rule Extraction in Unsupervised Anomaly Detection for Model Explainability: Application to OneClass SVMCode0
Explainable Artificial Intelligence (XAI) for 6G: Improving Trust between Human and Machine—0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
Towards Best Practice in Explaining Neural Network Decisions with LRPCode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Meaningful Data Sampling for a Faithful Local Explanation MethodCode0
Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms—0
How a minimal learning agent can infer the existence of unobserved variables in a complex environment—0
Towards Explainable Artificial Intelligence—0
On Evaluating Explainability Algorithms—0
Towards Explainable Neural-Symbolic Visual Reasoning—0
Towards a Rigorous Evaluation of XAI Methods on Time Series—0
The Challenge of Imputation in Explainable Artificial Intelligence Models—0
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