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AI Explainability, Trust, and Calibration / Explanation Form Design and Comprehension Effects

Users struggle to understand how natural language generation systems work, leading to interaction failures and wasted time.

lightbulbPractical problemExplanation Form Design and Comprehension Effects

Users struggle to understand how natural language generation systems work, leading to interaction failures and wasted time.

IUI '23Follow the Successful Herd: Towards Explanations for Improved Use and Mental Models of Natural Language Systems

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Noise or privacy protection weakens emotion recognition accuracy and explanation reliability.

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Users misjudge symptoms due to health information on social media, leading to incorrect self-diagnosis.

CHI '25Mining Evidence about Your Symptoms: Mitigating Availability Bias in Online Self-Diagnosis

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Users distrust or misunderstand explanations of autonomous driving system behavior.

CHI '25People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI

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Users feel anxious about health information from search engines, especially when misinformation triggers panic.

CHI '25How the Algorithmic Transparency of Search Engines Influences Health Anxiety: The Mediating Effects of Trust in Online Health Information Search

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Users struggle to obtain context-relevant, personalized stress management advice.

CHI '25CounterStress: Enhancing Stress Coping Planning through Counterfactual Explanations in Personal Informatics

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Data annotation is time-consuming, and existing tools struggle to align with users' cognitive approaches and annotation intent.

UIST '24MOCHA: Model Optimization through Collaborative Human-AI Alignment

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