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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 476–500 of 971 papers

TitleStatusHype
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models—0
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility—0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence—0
Unveiling the Potential of Counterfactuals Explanations in Employability—0
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study—0
Using agent-based models and EXplainable Artificial Intelligence (XAI) to simulate social behaviors and policy intervention scenarios: A case study of private well users in Ireland—0
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels—0
Using explainability to design physics-aware CNNs for solving subsurface inverse problems—0
Using Explainable AI to Cross-Validate Socio-economic Disparities Among Covid-19 Patient Mortality—0
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model—0
Utilizing Explainable AI for improving the Performance of Neural Networks—0
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)—0
Vector symbolic architectures for context-free grammars—0
Visual Explanations with Attributions and Counterfactuals on Time Series Classification—0
Visualizing the Diversity of Representations Learned by Bayesian Neural Networks—0
Visually Analyze SHAP Plots to Diagnose Misclassifications in ML-based Intrusion Detection—0
ViTmiX: Vision Transformer Explainability Augmented by Mixed Visualization Methods—0
VitrAI -- Applying Explainable AI in the Real World—0
VR-LENS: Super Learning-based Cybersickness Detection and Explainable AI-Guided Deployment in Virtual Reality—0
Weak Robust Compatibility Between Learning Algorithms and Counterfactual Explanation Generation Algorithms—0
WebXAII: an open-source web framework to study human-XAI interaction—0
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks—0
What Do We Want From Explainable Artificial Intelligence (XAI)? -- A Stakeholder Perspective on XAI and a Conceptual Model Guiding Interdisciplinary XAI Research—0
What Do You See? Evaluation of Explainable Artificial Intelligence (XAI) Interpretability through Neural Backdoors—0
What's meant by explainable model: A Scoping Review—0
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