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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
IsoEx: an explainable unsupervised approach to process event logs cyber investigation—0
Adversarial attacks and defenses in explainable artificial intelligence: A surveyCode2
Quantitative Analysis of Primary Attribution Explainable Artificial Intelligence Methods for Remote Sensing Image ClassificationCode0
Designing explainable artificial intelligence with active inference: A framework for transparent introspection and decision-making—0
Scalable Concept Extraction in Industry 4.0—0
From Robustness to Explainability and Back Again—0
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility—0
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables—0
Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples—0
Counterfactual Formulation of Patient-Specific Root Causes of Disease—0
An Experimental Investigation into the Evaluation of Explainability MethodsCode0
PIC-XAI: Post-hoc Image Captioning Explanation using SegmentationCode0
A Survey of Explainable AI and Proposal for a Discipline of Explanation Engineering—0
Unveiling the Potential of Counterfactuals Explanations in Employability—0
Unlocking the black box of CNNs: Visualising the decision-making process with PRISMCode1
Disproving XAI Myths with Formal Methods -- Initial Results—0
eXplainable Artificial Intelligence on Medical Images: A Survey—0
Achieving Diversity in Counterfactual Explanations: a Review and Discussion—0
Explainable Knowledge Distillation for On-device Chest X-Ray Classification—0
Exploring a Gradient-based Explainable AI Technique for Time-Series Data: A Case Study of Assessing Stroke Rehabilitation Exercises—0
Human Attention-Guided Explainable Artificial Intelligence for Computer Vision Models—0
Hardware Acceleration of Explainable Artificial Intelligence—0
Biomarker Investigation using Multiple Brain Measures from MRI through XAI in Alzheimer's Disease Classification—0
Widespread Increases in Future Wildfire Risk to Global Forest Carbon Offset Projects Revealed by Explainable AI—0
Metric Tools for Sensitivity Analysis with Applications to Neural Networks—0
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