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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 451–500 of 971 papers

TitleStatusHype
Leveraging Gene Expression Data and Explainable Machine Learning for Enhanced Early Detection of Type 2 Diabetes—0
Leveraging Learning Metrics for Improved Federated Learning—0
Linguistically Communicating Uncertainty in Patient-Facing Risk Prediction Models—0
LiteVR: Interpretable and Lightweight Cybersickness Detection using Explainable AI—0
LLMs for XAI: Future Directions for Explaining Explanations—0
Locality Guided Neural Networks for Explainable Artificial Intelligence—0
Locally Testing Model Detections for Semantic Global Concepts—0
Local Post-Hoc Explanations for Predictive Process Monitoring in Manufacturing—0
Logic Explanation of AI Classifiers by Categorical Explaining Functors—0
Longitudinal Distance: Towards Accountable Instance Attribution—0
Machine Learning For An Explainable Cost Prediction of Medical Insurance—0
Machine Learning in Sports: A Case Study on Using Explainable Models for Predicting Outcomes of Volleyball Matches—0
Machine Learning in Transaction Monitoring: The Prospect of xAI—0
Machine Learning Meets Transparency in Osteoporosis Risk Assessment: A Comparative Study of ML and Explainability Analysis—0
MAFUS: a Framework to predict mortality risk in MAFLD subjects—0
MAIR: Framework for mining relationships between research articles, strategies, and regulations in the field of explainable artificial intelligence—0
Manifold-based Shapley for SAR Recognization Network Explanation—0
Mapping the Potential of Explainable AI for Fairness Along the AI Lifecycle—0
Matching Representations of Explainable Artificial Intelligence and Eye Gaze for Human-Machine Interaction—0
Measuring Perceived Trust in XAI-Assisted Decision-Making by Eliciting a Mental Model—0
Measuring User Understanding in Dialogue-based XAI Systems—0
Mediation Challenges and Socio-Technical Gaps for Explainable Deep Learning Applications—0
Mediators: Conversational Agents Explaining NLP Model Behavior—0
Medical Image Denosing via Explainable AI Feature Preserving Loss—0
MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AI—0
Metric Tools for Sensitivity Analysis with Applications to Neural Networks—0
Mind the Gap! Bridging Explainable Artificial Intelligence and Human Understanding with Luhmann's Functional Theory of Communication—0
MiSuRe is all you need to explain your image segmentation—0
MixBoost: Improving the Robustness of Deep Neural Networks by Boosting Data Augmentation—0
Mobile Traffic Prediction at the Edge Through Distributed and Deep Transfer Learning—0
Motif-guided Time Series Counterfactual Explanations—0
MultiFIX: An XAI-friendly feature inducing approach to building models from multimodal data—0
Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer—0
Multimodal Explainable Artificial Intelligence: A Comprehensive Review of Methodological Advances and Future Research Directions—0
Multi-modal multi-objective model-based genetic programming to find multiple diverse high-quality models—0
Multivariate Probabilistic Forecasting of Intraday Electricity Prices using Normalizing Flows—0
Natural Example-Based Explainability: a Survey—0
Neural network interpretability with layer-wise relevance propagation: novel techniques for neuron selection and visualization—0
Neuro-symbolic Explainable Artificial Intelligence Twin for Zero-touch IoE in Wireless Network—0
nn2poly: An R Package for Converting Neural Networks into Interpretable Polynomials—0
Notion of Explainable Artificial Intelligence -- An Empirical Investigation from A Users Perspective—0
OAK4XAI: Model towards Out-Of-Box eXplainable Artificial Intelligence for Digital Agriculture—0
OMENN: One Matrix to Explain Neural Networks—0
Towards trustable SHAP scores—0
On Evaluating Explainability Algorithms—0
On the Connection between Game-Theoretic Feature Attributions and Counterfactual Explanations—0
On the Importance of Domain-specific Explanations in AI-based Cybersecurity Systems (Technical Report)—0
On the Injunction of XAIxArt—0
Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey—0
OPTDTALS: Approximate Logic Synthesis via Optimal Decision Trees Approach—0
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