lightbulbPractical problemExplanation Form Design and Comprehension Effects
Noise or privacy protection weakens emotion recognition accuracy and explanation reliability.Direction: AI Explainability, Trust, and Calibration
Explanation Form Design and Comprehension Effects
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2025
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25 items
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users misjudge symptoms due to health information on social media, leading to incorrect self-diagnosis.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users distrust or misunderstand explanations of autonomous driving system behavior.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users feel anxious about health information from search engines, especially when misinformation triggers panic.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to obtain context-relevant, personalized stress management advice.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
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to trust and understand AI decision explanations over the long term.UbiComp '24How to Validate Explainable Artificial Intelligence (XAI) in Longitudinal Studies?
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Current mental health recommendations lack practical feasibility for individual contexts.UbiComp '24Counterfactual Explanations in Personal Informatics for Personalized Mental Health Management
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Ordinary users struggle to understand AI decisions under complex participatory budgeting rules and lack trust.UbiComp '24"A User-Centric Exploration of Axiomatic Explainable AI in Participatory Budgeting"
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to understand AI decisions, especially incorrect predictions, affecting task performance.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Programmers struggle to quickly understand auto-generated code, especially when it involves complex or unfamiliar structures.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to understand counterfactual explanations for continuous features in AI systems, affecting trust and decisions.IUI '23Categorical and Continuous Features in Counterfactual Explanations of AI Systems
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to build long-term trust when facing erroneous AI recommendations.IUI '23It Seems Smart, but It Acts Stupid: Development of Trust in AI Advice in a Repeated Legal Decision-Making Task
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Non-expert healthcare workers and patients struggle to understand the sources of machine learning-predicted diabetes risk.IUI '23Directive Explanations for Monitoring the Risk of Diabetes Onset: Introducing Directive Data-Centric Explanations and Combinations to Support What-If Explorations
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to evaluate whether intelligent system recommendations are reliable in complex tasks.IUI '23Application of Subgoal-Based Explanations in Unreliable Intelligent Decision Support Systems
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Lay users struggle to understand machine learning model decision logic.IUI '23Investigating the Intelligibility of Plural Counterfactual Examples for Non-Expert Users: an Explanation User Interface Proposition and User Study
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
lightbulbPractical problemExplanation Form Design and Comprehension Effects
In domains such as healthcare, ordinary users struggle to understand AI decision logic for recognizing complex activities.UbiComp '23X-CHAR: A Concept-based Explainable Complex Human Activity Recognition Model
lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to understand smart home lighting system behavior, leading to distrust or abandonment.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Users struggle to obtain transparent, customized action recommendations from machine learning systems.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Adolescent users struggle to trust whether algorithmically recommended content in educational recommendation systems is reliable.lightbulbPractical problemExplanation Form Design and Comprehension Effects
General users cannot understand speech emotion recognition results and hesitate to use AI.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Lay users struggle to understand AI model behavior and tend to overestimate their own understanding.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Non-expert users struggle to understand how algorithms work and their internal states.lightbulbPractical problemExplanation Form Design and Comprehension Effects
Designers often face ambiguity and diverse interpretations when applying design probes.Related papers
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