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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 851–900 of 971 papers

TitleStatusHype
T5 for Hate Speech, Augmented Data and EnsembleCode0
Tackling the Accuracy-Interpretability Trade-off in a Hierarchy of Machine Learning Models for the Prediction of Extreme HeatwavesCode0
An Interpretable Deep Learning Approach for Skin Cancer CategorizationCode0
Beyond TreeSHAP: Efficient Computation of Any-Order Shapley Interactions for Tree EnsemblesCode0
Procedural Fairness in Machine LearningCode0
Visualizing and Understanding Contrastive LearningCode0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
ProtoShotXAI: Using Prototypical Few-Shot Architecture for Explainable AICode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Towards Rigorous Interpretations: a Formalisation of Feature AttributionCode0
Interpretable Machine Learning for Survival AnalysisCode0
Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black BoxCode0
Interpretable ML for Imbalanced DataCode0
Interpretable Summaries of Black Box Incident Triaging with Subgroup DiscoveryCode0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
XNAP: Making LSTM-based Next Activity Predictions Explainable by Using LRPCode0
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance PropagationCode0
midr: Learning from Black-Box Models by Maximum Interpretation DecompositionCode0
Intrinsic Subgraph Generation for Interpretable Graph based Visual Question AnsweringCode0
Visual explanation of black-box model: Similarity Difference and Uniqueness (SIDU) methodCode0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Quantifying Explainability of Saliency Methods in Deep Neural Networks with a Synthetic DatasetCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Explainable Debugger for Black-box Machine Learning ModelsCode0
Introducing User Feedback-based Counterfactual Explanations (UFCE)Code0
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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