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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 501–525 of 971 papers

TitleStatusHype
Optimizing Binary Decision Diagrams with MaxSAT for classification—0
Overlap Number of Balls Model-Agnostic CounterFactuals (ONB-MACF): A Data-Morphology-based Counterfactual Generation Method for Trustworthy Artificial Intelligence—0
Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System—0
Polynomial Threshold Functions of Bounded Tree-Width: Some Explainability and Complexity Aspects—0
Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques—0
Post-hoc explanation of black-box classifiers using confident itemsets—0
Precision of Individual Shapley Value Explanations—0
Predicting and explaining nonlinear material response using deep Physically Guided Neural Networks with Internal Variables—0
Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players—0
Prediction of Diblock Copolymer Morphology via Machine Learning—0
Principles of Explanation in Human-AI Systems—0
Privacy-Preserving Federated Learning with Differentially Private Hyperdimensional Computing—0
Privacy Risks and Preservation Methods in Explainable Artificial Intelligence: A Scoping Review—0
Probabilities of Causation for Continuous and Vector Variables—0
Proceedings of the 1st Workshop on Interactive Natural Language Technology for Explainable Artificial Intelligence (NL4XAI 2019)—0
QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum-Classical Neural Network—0
Qualitative Investigation in Explainable Artificial Intelligence: A Bit More Insight from Social Science—0
Quality Metrics for Transparent Machine Learning With and Without Humans In the Loop Are Not Correlated—0
Revealing Similar Semantics Inside CNNs: An Interpretable Concept-based Comparison of Feature Spaces—0
Reactive Model Correction: Mitigating Harm to Task-Relevant Features via Conditional Bias Suppression—0
Reasons, Values, Stakeholders: A Philosophical Framework for Explainable Artificial Intelligence—0
Refutation of Shapley Values for XAI -- Additional Evidence—0
Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence—0
Reinforcement Learning Tutor Better Supported Lower Performers in a Math Task—0
Reinforcing Clinical Decision Support through Multi-Agent Systems and Ethical AI Governance—0
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