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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 151–200 of 971 papers

TitleStatusHype
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical DataCode0
From Flexibility to Manipulation: The Slippery Slope of XAI EvaluationCode0
cito: An R package for training neural networks using torchCode0
Characterizing the contribution of dependent features in XAI methodsCode0
Explainable Learning with Gaussian ProcessesCode0
Explainable Debugger for Black-box Machine Learning ModelsCode0
Explainable expected goal models for performance analysis in football analyticsCode0
Explaining Deep Learning Models for Age-related Gait Classification based on time series accelerationCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex ModelsCode0
Algorithm-Agnostic Explainability for Unsupervised ClusteringCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Causal Discovery and Classification Using Lempel-Ziv ComplexityCode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
CEnt: An Entropy-based Model-agnostic Explainability Framework to Contrast Classifiers' DecisionsCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Challenges facing the explainability of age prediction models: case study for two modalitiesCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
An explainable three dimension framework to uncover learning patterns: A unified look in variable sulci recognitionCode0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Cartan moving frames and the data manifoldsCode0
Explainable Machine Learning for Breakdown Prediction in High Gradient RF CavitiesCode0
Class-Dependent Perturbation Effects in Evaluating Time Series AttributionsCode0
A Co-design Study for Multi-Stakeholder Job Recommender System ExplanationsCode0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
Explaining Local, Global, And Higher-Order Interactions In Deep LearningCode0
Clinical Domain Knowledge-Derived Template Improves Post Hoc AI Explanations in Pneumothorax ClassificationCode0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Coherent Local Explanations for Mathematical OptimizationCode0
CohEx: A Generalized Framework for Cohort ExplanationCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
Communicating Smartly in the Molecular Domain: Neural Networks in the Internet of Bio-Nano ThingsCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
False Sense of Security: Leveraging XAI to Analyze the Reasoning and True Performance of Context-less DGA ClassifiersCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
Finding the right XAI method -- A Guide for the Evaluation and Ranking of Explainable AI Methods in Climate ScienceCode0
Explainability of Machine Learning Models under Missing DataCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Assessing Fidelity in XAI post-hoc techniques: A Comparative Study with Ground Truth Explanations DatasetsCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Evaluating saliency methods on artificial data with different background typesCode0
GECOBench: A Gender-Controlled Text Dataset and Benchmark for Quantifying Biases in ExplanationsCode0
Explanations Based on Item Response Theory (eXirt): A Model-Specific Method to Explain Tree-Ensemble Model in Trust PerspectiveCode0
Conditional Feature Importance with Generative Modeling Using Adversarial Random ForestsCode0
EvalAttAI: A Holistic Approach to Evaluating Attribution Maps in Robust and Non-Robust ModelsCode0
Explainability in Music Recommender SystemsCode0
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