arrow_backBack to questions View categoryarrow_forward

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

How can combining individual predictions with population-level statistical distribution explanations improve healthcare workers' understanding of diabetes risk?

helpResearch questionExplanation 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

Similar questions

helpResearch questionExplanation Form Design and Comprehension Effects

How can reliable emotion analysis model explanations be generated under noisy conditions?

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

helpResearch questionExplanation Form Design and Comprehension Effects

Can decomposing speech features (e.g., loudness, pitch, speech rate) improve intuitiveness and credibility of model explanations?

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

helpResearch questionExplanation Form Design and Comprehension Effects

How can intuitive user-usable explanations be generated by comparing multiple emotional features?

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

helpResearch questionExplanation Form Design and Comprehension Effects

How does health-related information on social media trigger users' availability bias and affect self-diagnosis?

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

helpResearch questionExplanation Form Design and Comprehension Effects

Which chatbot-based symptom checker (CSC) most effectively mitigates availability bias triggered by social media?

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

helpResearch questionExplanation Form Design and Comprehension Effects

How do different cognitive intervention strategies (e.g., counterfactual thinking and evidence reflection) affect users' self-diagnosis accuracy?

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

How can combining individual predictions with population-level statistical distribution explanations improve healthcare workers' understanding of diabetes risk?

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