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

Non-expert healthcare workers and patients struggle to understand the sources of machine learning-predicted diabetes risk.

lightbulbPractical problemExplanation Form Design and Comprehension Effects

IUI '23Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations

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lightbulbPractical problemExplanation Form Design and Comprehension Effects

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

IUI '25Robust Relatable Explanations of Machine Learning with Disentangled Cue-specific Saliency

lightbulbPractical problemExplanation Form Design and Comprehension Effects

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

lightbulbPractical problemExplanation Form Design and Comprehension Effects

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

lightbulbPractical problemExplanation Form Design and Comprehension Effects

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

lightbulbPractical problemExplanation Form Design and Comprehension Effects

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

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

lightbulbPractical problemExplanation Form Design and Comprehension Effects

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

Non-expert healthcare workers and patients struggle to understand the sources of machine learning-predicted diabetes risk.

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