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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 201–250 of 971 papers

TitleStatusHype
Cartan moving frames and the data manifoldsCode0
Explainable Learning with Gaussian ProcessesCode0
An Accelerator for Rule Induction in Fuzzy Rough TheoryCode0
Interpretable ML for Imbalanced DataCode0
Applying Genetic Programming to Improve Interpretability in Machine Learning ModelsCode0
Interpreting End-to-End Deep Learning Models for Speech Source Localization Using Layer-wise Relevance PropagationCode0
CARE: Coherent Actionable Recourse based on Sound Counterfactual ExplanationsCode0
A Deep Dive into Perturbations as Evaluation Technique for Time Series XAICode0
iPDP: On Partial Dependence Plots in Dynamic Modeling ScenariosCode0
Is Conversational XAI All You Need? Human-AI Decision Making With a Conversational XAI AssistantCode0
Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AICode0
Explainable Artificial Intelligence for Improved Modeling of ProcessesCode0
CoXQL: A Dataset for Parsing Explanation Requests in Conversational XAI SystemsCode0
Less is More: Discovering Concise Network ExplanationsCode0
Explainable Artificial Intelligence for Bayesian Neural Networks: Towards trustworthy predictions of ocean dynamicsCode0
REVEX: A Unified Framework for Removal-Based Explainable Artificial Intelligence in VideoCode0
Data-Adaptive Discriminative Feature Localization with Statistically Guaranteed InterpretationCode0
Analyzing and Improving the Robustness of Tabular Classifiers using Counterfactual ExplanationsCode0
Explainability in Process Outcome Prediction: Guidelines to Obtain Interpretable and Faithful ModelsCode0
Explainable Authorship Identification in Cultural Heritage Applications: Analysis of a New PerspectiveCode0
Explainable Artificial Intelligence and Multicollinearity : A Mini Review of Current ApproachesCode0
Explainable Artificial Intelligence for Manufacturing Cost Estimation and Machining Feature VisualizationCode0
Explainable artificial intelligence approaches for brain-computer interfaces: a review and design spaceCode0
Explainable Artificial Intelligence for Dependent Features: Additive Effects of CollinearityCode0
Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG SignalsCode0
Deep Learning for Precision Agriculture: Post-Spraying Evaluation and Deposition EstimationCode0
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain InjuryCode0
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
Explainable AI for Comparative Analysis of Intrusion Detection ModelsCode0
Explainability of Machine Learning Models under Missing DataCode0
Explainability in Practice: Estimating Electrification Rates from Mobile Phone Data in SenegalCode0
Explainability of Predictive Process Monitoring Results: Can You See My Data Issues?Code0
Improving Deep Neural Network Classification Confidence using Heatmap-based eXplainable AICode0
On Formal Feature Attribution and Its ApproximationCode0
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AICode0
PIC-XAI: Post-hoc Image Captioning Explanation using SegmentationCode0
A novel approach to generate datasets with XAI ground truth to evaluate image modelsCode0
POTHER: Patch-Voted Deep Learning-Based Chest X-ray Bias Analysis for COVID-19 DetectionCode0
Bounded logit attention: Learning to explain image classifiersCode0
A comprehensive study on fidelity metrics for XAICode0
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
bLIMEy: Surrogate Prediction Explanations Beyond LIMECode0
An Experimental Investigation into the Evaluation of Explainability MethodsCode0
Relevant Irrelevance: Generating Alterfactual Explanations for Image ClassifiersCode0
Black Box Model Explanations and the Human Interpretability Expectations -- An Analysis in the Context of Homicide PredictionCode0
REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studyCode0
Acquiring Qualitative Explainable Graphs for Automated Driving Scene InterpretationCode0
Explainability in Music Recommender SystemsCode0
Selecting Robust Features for Machine Learning Applications using Multidata Causal DiscoveryCode0
Explainable Anomaly Detection for Industrial Control System CybersecurityCode0
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