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Effects of Explanations in AI-Assisted Decision Making: Principles and Comparisons
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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A Design Methodology for Trust Cue Calibration in Cognitive Agents
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Fair Statistical Communication in HCI
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Explanation in artificial intelligence: Insights from the social sciences
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Explaining black-box classifiers using post-hoc explanations-by-example: The effect of explanations and error-rates in XAI user studies
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Trust between humans and machines, and the design of decision aids
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Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
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The misunderstood limits of folk science: an illusion of explanatory depth
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Do scores from risk measures matter to jurors?
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Artificial Intelligence and Statistics
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Peeking Inside the Black Box: Visualizing Statistical Learning With Plots of Individual Conditional Expectation
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Addressing Forest Management Challenges by Refining Tree Cover Type Classification with Machine Learning Models
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Understanding the Effect of Accuracy on Trust in Machine Learning Models
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COGAM: Measuring and Moderating Cognitive Load in Machine Learning Model Explanations
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"Why is 'Chicago' deceptive?" Towards Building Model-Driven Tutorials for Humans
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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
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The Principles and Limits of Algorithm-in-the-Loop Decision Making
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Human Decision Making with Machine Assistance
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Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems
inferredcites →https://doi.org/10.1145/3377325.3377519openalex-snapshot
Evaluating saliency map explanations for convolutional neural networks
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I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI
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Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
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Does the Whole Exceed its Parts? The Effect of AI Explanations on Complementary Team Performance
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Interpretable Machine Learning
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Make It Stick: The science of successful learning
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Supporting Trust Calibration and the Effective Use of Decision Aids by Presenting Dynamic System Confidence Information
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Humans and Automation: Use, Misuse, Disuse, Abuse
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Feature-Based Explanations Don't Help People Detect Misclassifications of Online Toxicity
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Counterfactual Explanations Without Opening the Black Box: Automated Decisions and the GDPR
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Interpretable to Whom? A Role-based Model for Analyzing Interpretable Machine Learning Systems
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Incoming claims: where this work appears in other papers’ reference lists and arguments.
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