arrow_backBack to questions View categoryarrow_forward

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

Lay users struggle to understand machine learning model decision logic.

lightbulbPractical problemExplanation Form Design and Comprehension Effects

IUI '23Investigating the Intelligibility of Plural Counterfactual Examples for Non-Expert Users: an Explanation User Interface Proposition and User Study

Similar questions

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

Lay users struggle to understand machine learning model decision logic.

Similar questions

Related papers

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

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

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

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

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

Unraveling the Dilemma of AI Errors: Exploring the Effectiveness of Human and Machine Explanations for Large Language Models

Ivie: Lightweight Anchored Explanations of Just-Generated Code

The Effect of Explanation Design on User Perception of Smart Home Lighting Systems: A Mixed-method Investigation

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Explaining Recommendations in E-Learning, Effects on Adolescents' Trust

Towards Relatable Explainable AI with the Perceptual Process

I Think I Get Your Point, AI! The Illusion of Explanatory Depth in Explainable AI