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Counterfactual Explanations with Graded Interactivity: Effects on Mental-Model Formation and Explanation Satisfaction
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Interactive Explainable Intelligent Systems: Requirements, Design Principles, and Prototypical Implementation
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Interactive machine learning: experimental evidence for the human in the algorithmic loop
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Counterfactual explanations and how to find them: literature review and benchmarking
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Learning by Doing or Doing Without Learning? The Potentials and Challenges of Activity-Based Learning
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Explanation in artificial intelligence: Insights from the social sciences
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What do we want from Explainable Artificial Intelligence (XAI)? – A stakeholder perspective on XAI and a conceptual model guiding interdisciplinary XAI research
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The interactivity effect in multimedia learning
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From explainable to interactive AI: A literature review on current trends in human-AI interaction
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The role of user feedback in enhancing understanding and trust in counterfactual explanations for explainable AI
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The Meaning of Interactivity—Some Proposals for Definitions and Measures
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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A Literature Review on Applications of Explainable Artificial Intelligence (XAI)
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RIXA - Explaining Artificial Intelligence in Natural Language
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Toward a Deeper Understanding of the Role of Interaction in Information Visualization
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VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models
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AdViCE: Aggregated Visual Counterfactual Explanations for Machine Learning Model Validation
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The challenge of crafting intelligible intelligence
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Explaining machine learning classifiers through diverse counterfactual explanations
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Effect of confidence and explanation on accuracy and trust calibration in AI-assisted decision making
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Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems
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Are Explanations Helpful? A Comparative Study of the Effects of Explanations in AI-Assisted Decision-Making
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Understanding the Effect of Out-of-distribution Examples and Interactive Explanations on Human-AI Decision Making
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Why Am I Not Seeing It? Understanding Users’ Needs for Counterfactual Explanations in Everyday Recommendations
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What Did My AI Learn? How Data Scientists Make Sense of Model Behavior
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Explainable AI (XAI): Core Ideas, Techniques, and Solutions
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Understanding the User Perception and Experience of Interactive Algorithmic Recourse Customization
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Counterfactual Explanations and Algorithmic Recourses for Machine Learning: A Review
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Explainable Artificial Intelligence: Importance, Use Domains, Stages, Output Shapes, and Challenges
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Learning by doing and learning through play
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Nasa-Task Load Index (NASA-TLX); 20 Years Later
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The misunderstood limits of folk science: an illusion of explanatory depth
inferredcites →https://doi.org/10.1287/mnsc.2016.2643openalex-snapshot
Overcoming Algorithm Aversion: People Will Use Imperfect Algorithms If They Can (Even Slightly) Modify Them
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On Generating Plausible Counterfactual and Semi-Factual Explanations for Deep Learning
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Measures for explainable AI: Explanation goodness, user satisfaction, mental models, curiosity, trust, and human-AI performance
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A Systematic Review of Explainable Artificial Intelligence in Terms of Different Application Domains and Tasks
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An Overview of the Empirical Evaluation of Explainable AI (XAI): A Comprehensive Guideline for User-Centered Evaluation in XAI
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How people reason with counterfactual and causal explanations for Artificial Intelligence decisions in familiar and unfamiliar domains
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