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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 226–250 of 971 papers

TitleStatusHype
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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