helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can an interactive conversational XAI system be designed to meet users' knowledge levels, information needs, and task contexts?Direction: AI Explainability, Trust, and Calibration
Misinformation, Content Labeling, and Authenticity Trust
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2025
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49 items
helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can few-shot generation and filtering mechanisms improve diversity and quality of synthetic dialogue data?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
What innovative methods can reduce hallucination (misinformation) problems in generated dialogue?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Non-expert users struggle to understand and trust complex AI systems' decision rationales.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How do users perceive and respond to AI-generated false or misleading answers?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How do misleading answers (hallucinations) affect users' understanding and trust of AI-generated characters?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Do users' gender and experience affect their behavioral patterns when responding to AI hallucinations?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
When interacting with AI-generated characters, users often make wrong decisions due to hallucinated answers, and trust is damaged.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can behavioral interventions reduce misinformation spread in online environments?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
What theoretical models can be developed to support design and evaluation of behavioral interventions?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
What are the main reasons behavioral interventions fail, and how can these problems be avoided?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Users frequently encounter misinformation on social platforms, affecting decisions and trust.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Which artifacts (visual flaws) in diffusion model-generated images are easy or difficult for humans to detect?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How does human ability to detect diffusion model-generated images change over time and with scene complexity?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Can artifact classification methods quantify the realism of AI-generated images?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
The public struggles to distinguish highly realistic AI-generated fake images, potentially worsening information credibility crises.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Why do developers still frequently produce accessibility errors despite many assistive tools and standards?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can AI-assisted tools seamlessly integrate with developers' existing coding habits to improve accessibility compliance in code generation?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Can a multi-agent architecture such as multi-task LLMs detect and fix accessibility code defects in real time and improve developers' incidental learning?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
95.9% of websites have accessibility errors, affecting fair internet access for people with disabilities.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Can AI credibility indicators help detect misinformation and reduce its spread under peer and expert influence?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Does agreement or disagreement between experts and AI affect effectiveness of AI credibility indicators?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Does verifying experts' professional backgrounds change effectiveness of AI credibility indicators?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
What happens to AI credibility indicator effectiveness when AI model accuracy fluctuates?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Misinformation proliferates on social media, and users struggle to judge its credibility.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How do labeling designs for deepfakes and AI-generated content affect users' perception of content authenticity?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How effective are different label designs (language, icons, etc.) at improving trust in social media platforms?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Do AI-generated content labels significantly change users' interaction behaviors (likes, comments, shares) on social media?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Users struggle to discern the authenticity of AI-generated or modified content on social media.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How does self-consistency (evaluating consistency through diverse examples) improve semantic-level trustworthiness verification of LLM outputs?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can interactive systems help users verify semantic consistency and authenticity of long-text generation?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How does the self-consistency mechanism perform during users' verification of generated content?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Users struggle to judge the authenticity of AI-generated text and are easily misled.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How do users cognitively differ regarding content labeled as 'human-created,' 'AI-assisted,' or 'AI-generated'?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How do these labels affect users' judgments of content originality, credibility, and creator qualifications?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How do users understand the specific meaning of 'AI-assisted' or 'AI-generated'?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Users cannot accurately understand AI content labels, affecting trust in content and creators.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
In AI-assisted data analysis, how can analysts understand and verify AI-generated analytical results?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
During data validation, which key features help analysts better identify and correct errors?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
What behavioral patterns do analysts exhibit when validating AI-generated analytical results?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
AI-generated data analysis results may contain errors that are difficult to verify, undermining user trust.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How does AI affect recipients' trust when generating emails?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
What role does high interpersonal emphasis play in AI-generated emails?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How does trust change when AI and humans co-author emails?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Users may have low trust in highly emotional emails written by AI.helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can more faithful visual explanations of true targets be generated when image data is affected by systematic bias (e.g., blur, color distortion, or lighting variation)?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
How can multi-task learning reduce bias in model explanations and improve consistency?helpResearch questionMisinformation, Content Labeling, and Authenticity Trust
Can Debiased-CAM improve users' trust in AI system explanations under biased conditions?lightbulbPractical problemMisinformation, Content Labeling, and Authenticity Trust
Users struggle to trust AI-generated explanations, especially under image bias conditions.Related papers
IUI 2025
Conversational Explanations: Discussing Explainable AI with Non-AI Experts
Tong Zhang, Mengao Zhang, Wei Yan Low
IUI 2025
"You Always Get an Answer": Analyzing Users' Interaction with AI-Generated Personas Given Unanswerable Questions and Risk of Hallucination
Ilkka Kaate, Joni Salminen, Soon-Gyo Jung
CHI 2025
Behavior Change Interventions Combating Online Misinformation: A Scoping Review
Loukas Konstantinou, Evangelos Karapanos
CHI 2025
Characterizing Photorealism and Artifacts in Diffusion Model-Generated Images
Negar Kamali, Karyn Nakamura, Aakriti Kumar
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Deceptive Explanations by Large Language Models Lead People to Change their Beliefs About Misinformation More Often than Honest Explanations
Valdemar Danry, Pat Pataranutaporn, Matthew Groh
CHI 2025
Understanding the Effects of AI-based Credibility Indicators When People Are Influenced By Both Peers and Experts
Zhuoran Lu, Patrick Li, Weilong Wang
CHI 2025
Labeling Synthetic Content: User Perceptions of Label Designs for AI-Generated Content on Social Media
Dilrukshi Gamage, Dilki Sewwandi, Min Zhang
CHI 2024
RELIC: Investigating Large Language Model Responses using Self-Consistency
Furui Cheng, Vilém Zouhar, Simran Arora
CHI 2024
The Effects of Perceived AI Use On Content Perceptions
Irene Rae
CHI 2024
How Do Analysts Understand and Verify AI-Assisted Data Analyses?
Ken Gu, Ruoxi Shang, Tim Althoff
CHI 2022
Will AI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing
Yihe Liu, Anushk Mittal, Diyi Yang
CHI 2022
Debiased-CAM to mitigate image perturbations with faithful visual explanations of machine learning
Wencan Zhang, Mariella Dimiccoli, Brian Y Lim
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