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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 126–150 of 971 papers

TitleStatusHype
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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