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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 101–125 of 971 papers

TitleStatusHype
ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods—0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
Integrating Evidence into the Design of XAI and AI-based Decision Support Systems: A Means-End Framework for End-users in Construction—0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0—0
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
Meta-evaluating stability measures: MAX-Senstivity & AVG-SensitivityCode0
Assessing high-order effects in feature importance via predictability decomposition—0
REPEAT: Improving Uncertainty Estimation in Representation Learning ExplainabilityCode0
Discrete Subgraph Sampling for Interpretable Graph based Visual Question AnsweringCode0
FaceX: Understanding Face Attribute Classifiers through Summary Model ExplanationsCode0
Neural network interpretability with layer-wise relevance propagation: novel techniques for neuron selection and visualization—0
From Flexibility to Manipulation: The Slippery Slope of XAI EvaluationCode0
A Unified Framework for Evaluating the Effectiveness and Enhancing the Transparency of Explainable AI Methods in Real-World Applications—0
OMENN: One Matrix to Explain Neural Networks—0
Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videos—0
A Comprehensive Guide to Explainable AI: From Classical Models to LLMsCode2
Forma mentis networks predict creativity ratings of short texts via interpretable artificial intelligence in human and GPT-simulated raters—0
Explaining the Impact of Training on Vision Models via Activation Clustering—0
XAI and Android Malware Models—0
Guarding the Gate: ConceptGuard Battles Concept-Level Backdoors in Concept Bottleneck Models—0
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2Code1
Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes—0
Explainable Artificial Intelligence for Medical Applications: A Review—0
Adapting the Biological SSVEP Response to Artificial Neural Networks—0
X-DFS: Explainable Artificial Intelligence Guided Design-for-Security Solution Space Exploration—0
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