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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 176–200 of 971 papers

TitleStatusHype
Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects—0
Explainable Federated Bayesian Causal Inference and Its Application in Advanced ManufacturingCode0
Integrating Explainable AI for Effective Malware Detection in Encrypted Network Traffic—0
Found in Translation: semantic approaches for enhancing AI interpretability in face verification—0
Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity—0
Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Text—0
FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example GenerationCode0
Extending XReason: Formal Explanations for Adversarial Detection—0
Enhancing Cancer Diagnosis with Explainable & Trustworthy Deep Learning Models—0
The Role of XAI in Transforming Aeronautics and Aerospace Systems—0
A Review of Multimodal Explainable Artificial Intelligence: Past, Present and FutureCode0
ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods—0
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
Meta-evaluating stability measures: MAX-Senstivity & AVG-SensitivityCode0
Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual Explanations for Multivariate Time Series ClassificationCode0
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
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