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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 101–150 of 971 papers

TitleStatusHype
Explanations of Black-Box Model Predictions by Contextual Importance and UtilityCode0
ExplainReduce: Summarising local explanations via proxiesCode0
EXPLAN: Explaining Black-box Classifiers using Adaptive Neighborhood GenerationCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Explaining Deep Learning Models for Age-related Gait Classification based on time series accelerationCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNNCode0
Explaining How Deep Neural Networks Forget by Deep VisualizationCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Explainable expected goal models for performance analysis in football analyticsCode0
Explainable Learning with Gaussian ProcessesCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Explanations for Answer Set ProgrammingCode0
Explainable Machine Learning for Breakdown Prediction in High Gradient RF CavitiesCode0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Exploring specialization and sensitivity of convolutional neural networks in the context of simultaneous image augmentationsCode0
A comprehensive study on fidelity metrics for XAICode0
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
An Experimental Investigation into the Evaluation of Explainability MethodsCode0
An Accelerator for Rule Induction in Fuzzy Rough TheoryCode0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
Addressing the Scarcity of Benchmarks for Graph XAICode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Explainable Debugger for Black-box Machine Learning ModelsCode0
FreqRISE: Explaining time series using frequency maskingCode0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Explainability in Music Recommender SystemsCode0
Explainability of Machine Learning Models under Missing DataCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Evaluating saliency methods on artificial data with different background typesCode0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
A Co-design Study for Multi-Stakeholder Job Recommender System ExplanationsCode0
Ensuring Medical AI Safety: Explainable AI-Driven Detection and Mitigation of Spurious Model Behavior and Associated DataCode0
End-to-end Stroke imaging analysis, using reservoir computing-based effective connectivity, and interpretable Artificial intelligenceCode0
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
EAG-RS: A Novel Explainability-guided ROI-Selection Framework for ASD Diagnosis via Inter-regional Relation LearningCode0
Eliminating The Impossible, Whatever Remains Must Be TrueCode0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
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