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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

Showing 621630 of 971 papers

TitleStatusHype
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation0
Unraveling Pedestrian Fatality Patterns: A Comparative Study with Explainable AI0
Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models0
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Comprehensibility0
Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence0
Unveiling the Potential of Counterfactuals Explanations in Employability0
User-centric evaluation of explainability of AI with and for humans: a comprehensive empirical study0
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 Ireland0
Using Deep Learning and Explainable Artificial Intelligence in Patients' Choices of Hospital Levels0
Using explainability to design physics-aware CNNs for solving subsurface inverse problems0
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