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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 876–900 of 971 papers

TitleStatusHype
Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscienceCode0
Enhancing Cluster Analysis With Explainable AI and Multidimensional Cluster PrototypesCode0
Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and GoalsCode0
Energy-based Model for Accurate Shapley Value Estimation in Interpretable Deep Learning Predictive ModelingCode0
Explainable expected goal models for performance analysis in football analyticsCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex ModelsCode0
Rational Shapley ValuesCode0
XAI-P-T: A Brief Review of Explainable Artificial Intelligence from Practice to TheoryCode0
Explainable Learning with Gaussian ProcessesCode0
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph ExplainabilityCode0
Keep Your Friends Close and Your Counterfactuals Closer: Improved Learning From Closest Rather Than Plausible Counterfactual Explanations in an Abstract SettingCode0
Explainable Machine Learning for Breakdown Prediction in High Gradient RF CavitiesCode0
Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern ClassificationCode0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
Counterfactual Explanations as Interventions in Latent SpaceCode0
Acquiring Qualitative Explainable Graphs for Automated Driving Scene InterpretationCode0
Conditional Feature Importance with Generative Modeling Using Adversarial Random ForestsCode0
Less is More: Discovering Concise Network ExplanationsCode0
Let's Go to the Alien Zoo: Introducing an Experimental Framework to Study Usability of Counterfactual Explanations for Machine LearningCode0
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importanceCode0
Leveraging CAM Algorithms for Explaining Medical Semantic SegmentationCode0
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