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How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checking
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Visualizing and Understanding Convolutional Networks
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TweetCred: Real-Time Credibility Assessment of Content on Twitter
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Four Perspectives on Human Bias in Visual Analytics
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Visualization of Uncertainty and Reasoning
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Interacting meaningfully with machine learning systems: Three experiments
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Core mechanisms in ‘theory of mind’
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Steps to take before intelligent user interfaces become real
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The Epistemology of Fact Checking
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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
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Stop Clickbait: Detecting and preventing clickbaits in online news media
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Towards Interpretation of Recommender Systems with Sorted Explanation Paths
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The Role of Explanations on Trust and Reliance in Clinical Decision Support Systems
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Visualization Rhetoric: Framing Effects in Narrative Visualization
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ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
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IEEE Transactions on Visualization and Computer Graphics
inferredcites →https://doi.org/10.1109/vlhcc.2013.6645235openalex-snapshot
Too much, too little, or just right? Ways explanations impact end users' mental models
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Content in Context Improves Deception Detection Accuracy
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The spread of true and false news online
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Crowdsourcing user studies with Mechanical Turk
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The impact of social information on visual judgments
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Principles of Explanatory Debugging to Personalize Interactive Machine Learning
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Leveraging the Implicit Structure within Social Media for Emergent Rumor Detection
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Fake News Detection on Social Media
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A Deep Learning Approach for Population Estimation from Satellite Imagery
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Relevant Document Discovery for Fact-Checking Articles
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A Survey of Methods for Explaining Black Box Models
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XFake: Explainable Fake News Detector with Visualizations
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Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking
inferredcites →https://doi.org/10.1145/3397481.3450639openalex-snapshot
Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems
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A Survey of Watermarking Algorithms for Image Authentication
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Intelligibility and Accountability: Human Considerations in Context-Aware Systems
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Learning by Analogical Bootstrapping
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Social Media and Fake News in the 2016 Election
inferredcites →https://doi.org/10.1609/aaai.v32i1.11491openalex-snapshot
Anchors: High-Precision Model-Agnostic Explanations
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Examining CNN Representations With Respect to Dataset Bias
inferredcites →https://doi.org/10.1609/hcomp.v7i1.5284openalex-snapshot
The Effects of Meaningful and Meaningless Explanations on Trust and Perceived System Accuracy in Intelligent Systems
inferredcites →https://doi.org/10.1609/hcomp.v8i1.7464openalex-snapshot
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inferredcites →https://doi.org/10.1609/hcomp.v8i1.7469openalex-snapshot
The Role of Domain Expertise in User Trust and the Impact of First Impressions with Intelligent Systems
inferredcites →https://doi.org/10.1609/icwsm.v15i1.18072openalex-snapshot
Machine Learning Explanations to Prevent Overtrust in Fake News Detection
inferredcites →https://doi.org/10.18653/v1/d17-1317openalex-snapshot
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking
inferredcites →https://doi.org/10.18653/v1/n16-3020openalex-snapshot
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
inferredcites →https://doi.org/10.24963/ijcai.2017/371openalex-snapshot
Right for the Right Reasons: Training Differentiable Models by Constraining their Explanations
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Glove: Global Vectors for Word Representation
inferredcites →https://doi.org/10.4018/979-8-3693-7822-9.ch012openalex-snapshot
Explainable Artificial Intelligence (XAI)
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Towards A Rigorous Science of Interpretable Machine Learning
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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DARPA 's explainable AI ( XAI ) program: A retrospective
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Plain Language to Address Dimensionality in Feature-Contribution Explanations for End-Users
inferred← citeshttps://doi.org/10.1007/978-3-031-97007-8_5openalex-snapshot
XAI for Entertainment, Education, and Environment: Case Studies and Trends
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Survey and Review on AI for Personalized Learning
inferred← citeshttps://doi.org/10.1007/s00766-025-00437-6openalex-snapshot
Design of explanation user interfaces for interactive machine learning using the example of a knowledge graph-based approach to explainable process analysis
inferred← citeshttps://doi.org/10.1007/s12525-025-00799-3openalex-snapshot
Explainable AI for online disinformation detection: Insights from a design science research project
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Trust, distrust, and appropriate reliance in (X)AI: A conceptual clarification of user trust and survey of its empirical evaluation
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User judgment of an AI model is biased by its description: A study in a job interview training context
inferred← citeshttps://doi.org/10.1016/j.jss.2026.112827openalex-snapshot
Explanation strategies in smart environments: Effects on usability, system understanding, and task performance
inferred← citeshttps://doi.org/10.1080/02650487.2026.2661459openalex-snapshot
How the level of detail in mechanistic explanation affects customer responses to artificial intelligence-driven recommendations on social media platforms
inferred← citeshttps://doi.org/10.1109/iros60139.2025.11245659openalex-snapshot
Adapting Robot’s Explanation for Failures Based on Observed Human Behavior in Human-Robot Collaboration
inferred← citeshttps://doi.org/10.1109/ro-man57019.2023.10309394openalex-snapshot
Effects of Explanation Strategies to Resolve Failures in Human-Robot Collaboration
inferred← citeshttps://doi.org/10.1111/cgf.15108openalex-snapshot
Guided By AI: Navigating Trust, Bias, and Data Exploration in AI‐Guided Visual Analytics
inferred← citeshttps://doi.org/10.1145/3630106.3659031openalex-snapshot
The Role of Explainability in Collaborative Human-AI Disinformation Detection
inferred← citeshttps://doi.org/10.1145/3706598.3713277openalex-snapshot
Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking
inferred← citeshttps://doi.org/10.1145/3706599.3719998openalex-snapshot
Examining Interactions Between User Characteristics and Explanation Modalities on Inducing Complementarity
inferred← citeshttps://doi.org/10.1145/3733567.3735566openalex-snapshot
Beyond Transparency: Evaluating Explainability in AI-Supported Fact-Checking
inferred← citeshttps://doi.org/10.1145/3742413.3789089openalex-snapshot
Who Needs What Explanation? How User Traits Affect Explanation Effectiveness in AI-Assisted Decision-Making
inferred← citeshttps://doi.org/10.1145/3742413.3789134openalex-snapshot
Improving Human Verification of LLM Reasoning through Interactive Explanation Interfaces
inferred← citeshttps://doi.org/10.1145/3757577openalex-snapshot
Designing Effective AI Explanations for Misinformation Detection: A Comparative Study of Content, Social, and Combined Explanations
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LLM-Mediated XAI Explanations: An AI Advisor for Fast and Calibrated Judgments on Potential Misinformation
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Show me the evidence: Evaluating the role of evidence and natural language explanations in AI-supported fact-checking
inferred← citeshttps://doi.org/10.1145/3820155openalex-snapshot
Using Extraction and Evaluation of Explanations for Drug-Repurposing on Knowledge Graphs
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Effects of Explanations and Accuracy on Human Performance and Trust in AI-Assisted Anomaly Diagnosis Tasks
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The Role of Human Knowledge in Explainable AI
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Impact of knowledge management, innovation and cloud computing on research
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A Literature Survey on Vision Assistance System Based on Binocular Sensors for Visually Impaired Users
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Artificial Intelligence: The Future of Education
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