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A Multi-Component Framework for the Analysis and Design of Explainable Artificial Intelligence
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Grounding Visual Explanations
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Information Visualization: An Introduction
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Normality: Part descriptive, part prescriptive
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Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
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ImageNet: A large-scale hierarchical image database
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Importance Estimation for Neural Network Pruning
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Empirical Studies in Information Visualization: Seven Scenarios
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Explainable Artificial Intelligence
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A Unified Approach to Interpreting Model Predictions
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Transfer Learning for IoT Applications
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Finding Optimal Diverse Feature Sets with Alternative Feature Selection
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