HCI.TOPHCI, made easy

arrow_backBack to questions View categoryarrow_forward

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

How can time-series-based models effectively generate understandable counterfactual explanations?

helpResearch questionExplanation Form Design and Comprehension Effects

How can time-series-based models effectively generate understandable counterfactual explanations?

UbiComp '23X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model

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

Related papers

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

Harshavardhan Sunil Abichandani, Wencan Zhang, Brian Y Lim

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

Junti Zhang, Zicheng Zhu, Jingshu Li

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

Balint Gyevnar, Stephanie Droop, Tadeg Quillien

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

Yuheng Wu, Yujie Dong, Yi Mou

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

Gyuwon Jung, Uichin Lee

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

Marvin Pafla, Kate Larson, Mark Hancock

Ivie: Lightweight Anchored Explanations of Just-Generated Code

Litao Yan, Alyssa Hwang, Zhiyuan Wu

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

Jiaxin Dai, Chao Zhang, Dzmitry Aliakseyeu

GAM Coach: Towards Interactive and User-centered Algorithmic Recourse

Zijie J. Wang, Jennifer Wortman Vaughan, Rich Caruana

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

Jeroen Ooge, Shotallo Kato, Katrien Verbert

Towards Relatable Explainable AI with the Perceptual Process

Wencan Zhang, Brian Y Lim

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

Michael Chromik, Malin Eiband, Felicitas Buchner